Noise suppression assisted automatic speech recognition

This application claims the priority benefit of U.S. Provisional Application Ser. No. 61/346,851, titled “Noise Suppression Assisted Automatic Speech Recognition,” filed May 20, 2010, the disclosure of the aforementioned application is incorporated herein by reference.

Speech recognition systems have been used to convert spoken words into text. In medium and high noise environments, however, the accuracy of automatic speech recognition systems tends to degrade significantly. As a result, most speech recognition systems are used with audio captured in a noise-free environment.

Unlike speech recognition systems, a standard noise reduction strategy consists of strongly attenuating portions of the acoustic spectrum which are dominated by noise. Spectrum portions dominated by speech are preserved.

Strong attenuation of undesired spectrum portions is a valid strategy from the point of view of noise reduction and perceived output signal quality, it is not necessarily a good strategy for an automatic speech recognition system. In particular, the spectral regions strongly attenuated by noise suppression may have been necessary to extract features for speech recognition. As a result, the attenuation resulting from noise suppression corrupts the features of the speech signal more than the original noise signal. This corruption by the noise suppression of the speech signal, which is greater than the corruption caused by the added noise signal, causes the noise reduction algorithm to make automatic speech recognition results unusable.

The present technology may utilize noise suppression information to optimize or improve automatic speech recognition performed for a signal. Noise suppression may be performed on a noisy speech signal using a gain value. The gain to apply to the noisy signal as part of the noise suppression is selected to optimize speech recognition analysis of the resulting signal. The gain may be selected based on one or more features for a current sub band and time frame, as well as others. Noise suppression information may be provided to a speech recognition module to improve the robustness of the speech recognition analysis. Noise suppression information may also be used to encode and identify speech. Resources spent on automatic speech recognition such as a bit rate of a speech codec) may be selected based on the SNR.

An embodiment may enable processing of an audio signal. Sub-band signals may be generated from a received primary acoustic signal and a secondary acoustic signal. One or more features may be determined for a sub-band signal. Noise suppression information may be determined based the one or more features to a speech recognition module.

FIG. 1 illustrates an environment in which the present technology may be utilized.

FIG. 2 is a block diagram of an exemplary audio device.

FIG. 3 is a block diagram of an exemplary audio processing system.

FIG. 4 is a flow chart of an exemplary method for performing speech recognition based on noise suppression information.

FIG. 5 is a flow chart of an exemplary method for performing noise suppression on a sub band signal.

FIG. 6 is a flow chart of an exemplary method for providing noise suppression information to a speech recognition module.

The present technology may utilize noise suppression information to optimize or improve automatic speech recognition performed for a signal. Noise suppression may be performed on a noisy speech signal using a gain value. The gain to apply to the noisy signal as part of the noise suppression is selected to optimize speech recognition analysis of the resulting signal. The gain may be selected based on one or more features for a current sub band and time frame, as well as others.

Noise suppression information may be provided to a speech recognition module to improve the robustness of the speech recognition analysis. Noise suppression information may include voice activity detection (VAD) information, such as for example noise, an indication of whether a signal includes speech, an indication of a speech to noise ration (SNR) for a signal, and other information. Noise suppression information may also be used to encode and identify speech. Resources spent on automatic speech recognition such as a bit rate of a speech codec) may be selected based on the SNR.

FIG. 1 is an illustration of an environment in which embodiments of the present technology may be used. A user may act as an audio (speech) source 102 to an audio device 104 . The exemplary audio device 104 includes two microphones: a primary microphone 106 relative to the audio source 102 and a secondary microphone 108 located a distance away from the primary microphone 106 . Alternatively, the audio device 104 may include a single microphone. In yet other embodiments, the audio device 104 may include more than two microphones, such as for example three, four, five, six, seven, eight, nine, ten or even more microphones.

The primary microphone 106 and secondary microphone 108 may be omni-directional microphones. Alternatively embodiments may utilize other forms of microphones or acoustic sensors, such as directional microphones.

While the microphones 106 and 108 receive sound (i.e. acoustic signals) from the audio source 102 , the microphones 106 and 108 also pick up noise 112 . Although the noise 110 is shown coming from a single location in FIG. 1 , the noise 110 may include any sounds from one or more locations that differ from the location of audio source 102 , and may include reverberations and echoes. The noise 110 may be stationary, non-stationary, and/or a combination of both stationary and non-stationary noise.

Some embodiments may utilize level differences (e.g. energy differences) between the acoustic signals received by the two microphones 106 and 108 . Because the primary microphone 106 is much closer to the audio source 102 than the secondary microphone 108 in a close-talk use case, the intensity level is higher for the primary microphone 106 , resulting in a larger energy level received by the primary microphone 106 during a speech/voice segment, for example.

The level difference may then be used to discriminate speech and noise in the time-frequency domain. Further embodiments may use a combination of energy level differences and time delays to discriminate speech. Based on binaural cue encoding, speech signal extraction or speech enhancement may be performed.

FIG. 2 is a block diagram of an exemplary audio device 104 . In the illustrated embodiment, the audio device 104 includes a receiver 200 , a processor 202 , the primary microphone 106 , an optional secondary microphone 108 , an audio processing system 210 , and an output device 206 . The audio device 104 may include further or other components necessary for audio device 104 operations. Similarly, the audio device 104 may include fewer components that perform similar or equivalent functions to those depicted in FIG. 2 .

Processor 202 may execute instructions and modules stored in a memory (not illustrated in FIG. 2 ) in the audio device 104 to perform functionality described herein, including noise reduction for an acoustic signal, speech recognition, and other functionality. Processor 202 may include hardware and software implemented as a processing unit, which may process floating point operations and other operations for the processor 202 .

The exemplary receiver 200 may include an acoustic sensor configured to receive and transmit a signal to and from a communications network. In some embodiments, the receiver 200 may include an antenna device. The signal received may be forwarded to the audio processing system 210 to reduce noise using the techniques described herein, and provide an audio signal to the output device 206 . Similarly, a signal received by one or more of primary microphone 106 and secondary microphone 108 may be processed for noise suppression and ultimately transmitted to a communications network via receiver 200 . Hence, the present technology may be used in one or both of the transmit and receive paths of the audio device 104 .

The audio processing system 210 is configured to receive the acoustic signals from an acoustic source via the primary microphone 106 and secondary microphone 108 (or a far-end signal via receiver 200 ) and process the acoustic signals. Processing may include performing noise reduction within an acoustic signal and speech recognition for an acoustic signal. The audio processing system 210 is discussed in more detail below.

The primary and secondary microphones 106 , 108 may be spaced a distance apart in order to allow for detecting an energy level difference, time difference or phase difference between them. The acoustic signals received by primary microphone 106 and secondary microphone 108 may be converted into electrical signals (i.e. a primary electrical signal and a secondary electrical signal). The electrical signals may themselves be converted by an analog-to-digital converter (not shown) into digital signals for processing in accordance with some embodiments. In order to differentiate the acoustic signals for clarity purposes, the acoustic signal received by the primary microphone 106 is herein referred to as the primary acoustic signal, while the acoustic signal received from by the secondary microphone 108 is herein referred to as the secondary acoustic signal. The primary acoustic signal and the secondary acoustic signal may be processed by the audio processing system 210 to produce a signal with an improved signal-to-noise ratio. It should be noted that embodiments of the technology described herein may be practiced utilizing only the primary microphone 106 .

The output device 206 is any device which provides an audio output to the user. For example, the output device 206 may include a speaker, an earpiece of a headset or handset, or a speaker on a conference device.

In various embodiments, where the primary and secondary microphones are omni-directional microphones that are closely-spaced (e.g., 1-2 cm apart), a beamforming technique may be used to simulate forwards-facing and backwards-facing directional microphones. The level difference may be used to discriminate speech and noise in the time-frequency domain which can be used in noise reduction.

FIG. 3 is a block diagram of an exemplary audio processing system 210 for performing noise reduction and automatic speech recognition. In exemplary embodiments, the audio processing system 210 is embodied within a memory device within audio device 104 . The audio processing system 210 may include a frequency analysis module 302 , a feature extraction module 304 , a source inference engine module 306 , gain data store 307 , mask selector module 308 , noise canceller module 310 , modifier module 312 , reconstructor module 314 , and automatic speech recognition 316 . Audio processing system 210 may include more or fewer components than illustrated in FIG. 3 , and the functionality of modules may be combined or expanded into fewer or additional modules. Exemplary lines of communication are illustrated between various modules of FIG. 3 , and in other figures herein. The lines of communication are not intended to limit which modules are communicatively coupled with others, nor are they intended to limit the number of and type of signals communicated between modules.

In operation, acoustic signals received from the primary microphone 106 and second microphone 108 are converted to electrical signals, and the electrical signals are processed through frequency analysis module 302 . The acoustic signals may be pre-processed in the time domain before being processed by frequency analysis module 302 . Time domain pre-processing may include applying input limiter gains, speech time stretching, and filtering using an FIR or IIR filter.

The frequency analysis module 302 receives the acoustic signals and mimics the frequency analysis of the cochlea (e.g., cochlear domain) to generate sub-band signals, simulated by a filter bank. The frequency analysis module 302 separates each of the primary and secondary acoustic signals into two or more frequency sub-band signals. A sub-band signal is the result of a filtering operation on an input signal, where the bandwidth of the filter is narrower than the bandwidth of the signal received by the frequency analysis module 302 . The filter bank may be implemented by a series of cascaded, complex-valued, first-order IIR filters. Alternatively, other filters such as short-time Fourier transform (STFT), sub-band filter banks, modulated complex lapped transforms, cochlear models, wavelets, etc., can be used for the frequency analysis and synthesis. The samples of the frequency sub-band signals may be grouped sequentially into time frames (e.g. over a predetermined period of time). For example, the length of a frame may be 4 ms, 8 ms, or some other length of time. In some embodiments there may be no frame at all. The results may include sub-band signals in a fast cochlea transform (FCT) domain.

The sub-band frame signals are provided from frequency analysis module 302 to an analysis path sub-system 320 and a signal path sub-system 330 . The analysis path sub-system 320 may process the signal to identify signal features, distinguish between speech components and noise components of the sub-band signals, and determine a signal modifier. The signal path sub-system 330 is responsible for modifying sub-band signals of the primary acoustic signal by reducing noise in the sub-band signals. Noise reduction can include applying a modifier, such as a multiplicative gain mask determined in the analysis path sub-system 320 , or by subtracting components from the sub-band signals. The noise reduction may reduce noise and preserve the desired speech components in the sub-band signals.

Signal path sub-system 330 includes noise canceller module 310 and modifier module 312 . Noise canceller module 310 receives sub-band frame signals from frequency analysis module 302 . Noise canceller module 310 may subtract (e.g., cancel) a noise component from one or more sub-band signals of the primary acoustic signal. As such, noise canceller module 310 may output sub-band estimates of noise components in the primary signal and sub-band estimates of speech components in the form of noise-subtracted sub-band signals.

Noise canceller module 310 may provide noise cancellation, for example in systems with two-microphone configurations, based on source location by means of a subtractive algorithm. Noise canceller module 310 may also provide echo cancellation and is intrinsically robust to loudspeaker and Rx path non-linearity. By performing noise and echo cancellation (e.g., subtracting components from a primary signal sub-band) with little or no voice quality degradation, noise canceller module 310 may increase the speech-to-noise ratio (SNR) in sub-band signals received from frequency analysis module 302 and provided to modifier module 312 and post filtering modules. The amount of noise cancellation performed may depend on the diffuseness of the noise source and the distance between microphones, both of which contribute towards the coherence of the noise between the microphones, with greater coherence resulting in better cancellation.

Noise canceller module 310 may be implemented in a variety of ways. In some embodiments, noise canceller module 310 may be implemented with a single NPNS module. Alternatively, Noise canceller module 310 may include two or more NPNS modules, which may be arranged for example in a cascaded fashion.

An example of noise cancellation performed in some embodiments by the noise canceller module 310 is disclosed in U.S. patent application Ser. No. 12/215,980, entitled “System and Method for Providing Noise Suppression Utilizing Null Processing Noise Subtraction,” filed Jun. 30, 2008, U.S. application Ser. No. 12/422,917, entitled “Adaptive Noise Cancellation,” filed Apr. 13, 2009, and U.S. application Ser. No. 12/693,998, entitled “Adaptive Noise Reduction Using Level Cues,” filed Jan. 26, 2010, the disclosures of which are each incorporated herein by reference.

The feature extraction module 304 of the analysis path sub-system 320 receives the sub-band frame signals derived from the primary and secondary acoustic signals provided by frequency analysis module 302 as well as the output of NPNS module 310 . Feature extraction module 304 may compute frame energy estimations of the sub-band signals, inter-microphone level differences (ILD), inter-microphone time differences (ITD) and inter-microphones phase differences (IPD) between the primary acoustic signal and the secondary acoustic signal, self-noise estimates for the primary and second microphones, as well as other monaural or binaural features which may be utilized by other modules, such as pitch estimates and cross-correlations between microphone signals. The feature extraction module 304 may both provide inputs to and process outputs from NPNS module 310 .

NPNS module may provide noise cancelled sub-band signals to the ILD block in the feature extraction module 304 . Since the ILD may be determined as the ratio of the NPNS output signal energy to the secondary microphone energy, ILD is often interchangeable with Null Processing Inter-microphone Level Difference (NP-ILD). “Raw-ILD” may be used to disambiguate a case where the ILD is computed from the “raw” primary and secondary microphone signals.

Determining energy level estimates and inter-microphone level differences is discussed in more detail in U.S. patent application Ser. No. 11/343,524, entitled “System and Method for Utilizing Inter-Microphone Level Differences for Speech Enhancement”, which is incorporated by reference herein.

Source inference engine module 306 may process the frame energy estimations provided by feature extraction module 304 to compute noise estimates and derive models of the noise and speech in the sub-band signals. Source inference engine module 306 adaptively estimates attributes of the acoustic sources, such as their energy spectra of the output signal of the NPNS module 310 . The energy spectra attribute may be utilized to generate a multiplicative mask in mask generator module 308 .

The source inference engine module 306 may receive the NP-ILD from feature extraction module 304 and track the NP-ILD probability distributions or “clusters” of the target audio source 102 , background noise and optionally echo.

This information is then used, along with other auditory cues, to define classification boundaries between source and noise classes. The NP-ILD distributions of speech, noise and echo may vary over time due to changing environmental conditions, movement of the audio device 104 , position of the hand and/or face of the user, other objects relative to the audio device 104 , and other factors. The cluster tracker adapts to the time-varying NP-ILDs of the speech or noise source(s).

An example of tracking clusters by a cluster tracker module is disclosed in U.S. patent application Ser. No. 12/004,897, entitled “System and method for Adaptive Classification of Audio Sources,” filed on Dec. 21, 2007, the disclosure of which is incorporated herein by reference.

Source inference engine module 306 may include a noise estimate module which may receive a noise/speech classification control signal from the cluster tracker module and the output of noise canceller module 310 to estimate the noise N(t,w), wherein t is a point in time and W represents a frequency or sub-band. A speech to noise ratio (SNR) can be generated by source inference engine module 306 from the noise estimate and a speech estimate, and the SNR can be provided to other modules within the audio device, such as automatic speech recognition module 316 and mask selector 308 .

Gain data store 307 may include one or more stored gain values and may communicate with mask selector 308 . Each stored gain may be associated with a set of one or more features. An exemplary set of features may include a speech to noise ratio and a frequency (i.e., a center frequency for a sub band). Other feature data may also be stored in gain store 307 . Each gain stored in gain data store 307 may, when applied to a sub-band signal, provide as close to a clean speech signal as possible. Though the gains provide a speech signal with a reduced amount of noise, they may not provide the perceptually most desirable sounding speech.

In some embodiments, each gain stored in gain store 307 may be optimized for a set of features, such as for example a particular frequency and speech to noise ratio. For example, to determine an optimal gain value for a particular combination of features, a known speech spectrum may be combined with noise at various speech to noise ratios. Because the energy spectrum and noise are known, a gain can be determined which suppress the combined speech-noise signal into a clean speech signal which is ideal for speech recognition. In some embodiments, the gain is configured to suppress the speech-noise signal such that noise is reduced but no portion of the speech signal is attenuated or degraded. These gains derived from the combined signals for a known SNR are stored in the gain data store for different combination of frequency and speech to noise ratio.

Mask selector 308 may receive a set of one or more features and/or other data from source inference engine 306 , query gain data store 307 for a gain associated with a particular set of features and/or other data, and provide an accessed gain to modifier 312 . For example, for a particular sub band, mask selector 308 may receive a particular speech to noise ratio from source inference engine 306 for the particular sub band in the current frame. Mask selector 308 may then query data store 307 for a gain that is associated with the combination of the speech to noise ratio and the current sub band center frequency. Mask selector 308 receives the corresponding gain from gain data store 307 and provides the gain to modifier 312 .

The accessed gain may be applied to the estimated noise subtracted sub-band signals provided, for example as a multiplicative mask, by noise canceller 310 to modifier 312 . The modifier module 312 multiplies the gain masks to the noise-subtracted sub-band signals of the primary acoustic signal output by the noise canceller module 310 . Applying the mask reduces energy levels of noise components in the sub-band signals of the primary acoustic signal and results in noise reduction.

Modifier module 312 receives the signal path cochlear samples from noise canceller module 310 and applies a gain mask received from mask selector 308 to the received samples. The signal path cochlear samples may include the noise subtracted sub-band signals for the primary acoustic signal. The gain mask provided by mask selector 308 may vary quickly, such as from frame to frame, and noise and speech estimates may vary between frames. To help address the variance, the upwards and downwards temporal slew rates of the mask may be constrained to within reasonable limits by modifier 312 . The mask may be interpolated from the frame rate to the sample rate using simple linear interpolation, and applied to the sub-band signals by multiplicative noise suppression. Modifier module 312 may output masked frequency sub-band signals.

Reconstructor module 314 may convert the masked frequency sub-band signals from the cochlea domain back into the time domain. The conversion may include adding the masked frequency sub-band signals and phase shifted signals. Alternatively, the conversion may include multiplying the masked frequency sub-band signals with an inverse frequency of the cochlea channels. Once conversion to the time domain is completed, the synthesized acoustic signal may be output to the user via output device 206 and/or provided to a codec for encoding.

In some embodiments, additional post-processing of the synthesized time domain acoustic signal may be performed. For example, comfort noise generated by a comfort noise generator may be added to the synthesized acoustic signal prior to providing the signal to the user. Comfort noise may be a uniform constant noise that is not usually discernable to a listener (e.g., pink noise). This comfort noise may be added to the synthesized acoustic signal to enforce a threshold of audibility and to mask low-level non-stationary output noise components. In some embodiments, the comfort noise level may be chosen to be just above a threshold of audibility and may be settable by a user. In some embodiments, the mask generator module 308 may have access to the level of comfort noise in order to generate gain masks that will suppress the noise to a level at or below the comfort noise.

Automatic speech recognition module 316 may perform a speech recognition analysis on the reconstructed signal output by reconstructor 314 . Automatic speech recognition module 316 may receive a voice activity detection (VAD) signal as well as a speech to noise (SNR) ratio indication or other noise suppression information from source inference engine 306 . The information received from source information engine 306 , such as the VAD and SNR, may be used to optimize the speech recognition process performed by automatic speech recognition module 316 . Speech recognition module 316 is discussed in more detail below.

The system of FIG. 3 may process several types of signals received by an audio device. The system may be applied to acoustic signals received via one or more microphones. The system may also process signals, such as a digital Rx signal, received through an antenna or other connection.

An exemplary system which may be used to implement at least a portion of audio processing system 210 is described in U.S. patent application Ser. No. 12/832,920, titled “Multi-Microphone Robust Noise Suppression,” filed Jul. 8, 2010, the disclosure of which is incorporated herein by reference

FIG. 4 is a flow chart of an exemplary method for performing speech recognition based on noise suppression information. First, a primary acoustic signal and a secondary acoustic signal are received at step 410 . The signals may be received through microphones 106 and 108 of audio device 104 . Sub band signals may then be generated from the primary acoustic signal and secondary acoustic signal at step 420 . The received signals may be converted to sub band signals by frequency analysis module 302 .

A feature is determined for a sub band signal at step 430 . Feature extractor 304 may extract features for each sub band in the current frame or the frame as a whole. Features may include a speech energy level for a particular sub band noise level, pitch, and other features. Noise suppression information is then generated from the features at step 440 . The noise suppression information may be generated and output by source inference engine 306 from features received from feature extraction module 304 . The noise suppression information may include an SNR ratio for each sub band in the current frame, a VAD signal for the current frame, ILD, and other noise suppression information.

Noise suppression may be performed on a sub band signal based on noise suppression information at step 450 . The noise suppression may include accessing a gain value based on one or more features and applying the gain to a sub band acoustic signal. Performing noise suppression on a sub band signal is discussed in more detail below with respect to the method of FIG. 5 . Additionally, noise suppression performed on a sub band signal may include performing noise cancellation by noise canceller 310 in the audio processing system of FIG. 3 .

Noise suppression information may be provided to speech recognition module 316 at step 460 . Speech recognition module 316 may receive noise suppression information to assist with speech recognition. Providing noise suppression information to speech recognition module 316 is discussed in more detail below with respect to FIG. 6 .

Speech recognition is automatically performed based on the noise suppression information at step 470 . The speech recognition process may be optimized based on the noise suppression information. Performing speech recognition based on noise suppression information may include modulating a bit rate of a speech encoder or decoder based on a speech to noise ratio for a particular frame. In some embodiments, the bit rate is decreased when the speech to noise ratio is large. In some embodiments, speech recognition based on noise suppression may include setting a node search depth level by a speech recognition module based on a speech to noise ratio for a current frame. The node search depth level, for example, may be decreased when the speech to noise ratio is large.

FIG. 5 is a flow chart of an exemplary method for performing noise suppression on a sub band signal. The method of FIG. 5 provides more detail for step 450 in the method of FIG. 4 . A speech to noise ratio (SNR) for a sub band is accessed at step 510 . The SNR may be received by mask selector 308 from source inference engine 306 . Mask selector 308 also has access to sub band information for the sub band being considered.

A gain which corresponds to the sub band signal frequency and the signal of the noise ratio is accessed at step 520 . The gain is accessed by mask selector 308 from gain data store 307 and may correspond to a particular sub band signal frequency and SNR. The accessed gain is then applied to one or more sub band frequencies at step 530 . The accessed gain may be provided to modifier 312 which then applies the gain to a sub band which may or may not be have undergone noise cancellation.

FIG. 6 is a flow chart of an exemplary method for providing noise suppression information to a speech recognition module. The method of FIG. 6 may provide more detail for step 460 than the method of FIG. 4 . A determination as to whether speech is detected in a primary acoustic signal based on one or more features is performed at step 610 . The detection may include detecting whether speech is or is not present within the signal within the current frame. In some embodiments, an SNR for the current sub band or for all sub bands may be compared to a threshold level. If the SNR is above the threshold value, then speech may be detected to be present in the primary acoustic signal. If the SNR is not above the threshold value, then speech may be determined to not be present in the current frame.

Each of steps 620 - 640 describe how speech recognition may be optimized based on noise suppression or noise suppression information and may be performed in combination or separately. Hence, in some embodiments, only one of step 620 - 640 may be performed. In some embodiments, more than just one of steps 620 - 640 may be performed when providing noise suppression information to a speech recognition module.

A speech recognition module is provided with a noise signal if a speech is not detected in a current frame of a signal at step 620 . For example, if the determination is made that speech is not present in the current frame of a reconstructed signal, a noise signal is provided to acoustic speech recognition module 316 in order to ensure that no false positive for speech detection occurs. The noise signal may be any type of signal that has a high likelihood of not being mistaken for speech by the speech recognition module.

A speech recognition module may be provided with an indication that speech is present in the acoustic signal at step 630 . In this case, automatic speech recognition module 316 may be provided with a VAD signal provided by source inference engine 306 . The VAD signal may indicate whether or not speech is present in the signal provided to automatic speech recognition module 316 . Automatic speech recognition module 316 may use the VAD signal to determine whether or not to perform speech recognition on the signal.

A speech to noise ratio (SNR) signal may be provided for the current frame and/or sub band to the speech recognition module at step 640 . In this case, the SNR may provide a value within a range of values indicating whether or not speech is present. This may help the automatic speech recognition module learn when to expend resources to recognize speech and when not to.

The above described modules, including those discussed with respect to FIG. 3 , may include instructions stored in a storage media such as a machine readable medium (e.g., computer readable medium). These instructions may be retrieved and executed by the processor 202 to perform the functionality discussed herein. Some examples of instructions include software, program code, and firmware. Some examples of storage media include memory devices and integrated circuits.

While the present invention is disclosed by reference to the preferred embodiments and examples detailed above, it is to be understood that these examples are intended in an illustrative rather than a limiting sense. It is contemplated that modifications and combinations will readily occur to those skilled in the art, which modifications and combinations will be within the spirit of the invention and the scope of the following claims.