Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
2000-10-18
2004-11-30
Dorvil, Richemond (Department: 2654)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S204000, C704S233000
Reexamination Certificate
active
06826528
ABSTRACT:
BACKGROUND
1. Field of the Invention
This invention relates generally to electronic speech detection systems, and relates more particularly to a method for implementing a noise suppressor in a speech recognition system.
2. Description of the Background Art
Implementing an effective and efficient method for system users to interface with electronic devices is a significant consideration of system designers and manufacturers. Human speech recognition is one promising technique that allows a system user to effectively communicate with selected electronic devices, such as digital computer systems. Speech generally consists of one or more spoken utterances which each may include a single word or a series of closely-spaced words forming a phrase or a sentence. In practice, speech detection systems typically determine the endpoints (the beginning and ending points) of a spoken utterance to accurately identify the specific sound data intended for analysis.
Conditions with significant ambient background-noise levels present additional difficulties when implementing a speech detection system. Examples of such noisy conditions may include speech recognition in automobiles or in certain manufacturing facilities. In such user applications, in order to accurately analyze a particular utterance, a speech recognition system may be required to selectively differentiate between a spoken utterance and the ambient background noise.
Referring now to FIG.
1
(
a
), an exemplary waveform diagram for one embodiment of noisy speech
112
is shown. In addition, FIG.
1
(
b
) depicts an exemplary waveform diagram for one embodiment of speech
114
without noise. Similarly, FIG.
1
(
c
) shows an exemplary waveform diagram for one embodiment of noise
116
without speech
114
. In practice, noisy speech
112
of FIG.
1
(
a
) is therefore typically comprised of several components, including speech
114
of FIG. (
1
(
b
) and noise
116
of FIG.
1
(
c
). In FIGS.
1
(
a
),
1
(
b
), and
1
(
c
), waveforms
112
,
114
, and
116
are presented for purposes of illustration only. The present invention may readily function and incorporate various other embodiments of noisy speech
112
, speech
114
, and noise
116
.
An important measurement in speech detection systems is the signal-to-noise ratio (SNR) which specifies the amount of noise present in relation to a given signal. For example, the SNR of noisy speech
112
in FIG.
1
(
a
) may be expressed as the ratio of noisy speech
112
divided by noise
116
of FIG.
1
(
c
). Many speech detection systems tend to function unreliably in conditions of high background noise when the SNR drops below an acceptable level. For example, if the SNR of a given speech detection system drops below a certain value (for example, 0 decibels), then the accuracy of the speech detection function may become significantly degraded.
Various methods have been proposed for speech enhancement and noise suppression. For example, one known method for speech enhancement is Wiener filtering. Inverse filtering based on all-pole models has also been reported as a suitable method for noise suppression. However, the foregoing methods are not entirely satisfactory in certain relevant applications, and thus they may not perform adequately in particular implementations. From the foregoing discussion, it therefore becomes apparent that suppressing ambient background noise to improve the signal-to-noise ratio in a speech detection system is a significant consideration of system designers and manufacturers of speech detection systems.
SUMMARY OF THE INVENTION
In accordance with the present invention, a method is disclosed for suppressing background noise in a speech detection system. In one embodiment, a feature extractor in a speech detector initially receives noisy speech data that is preferably generated by a sound sensor, an amplifier and an analog-to-digital converter. In the preferred embodiment, the speech detector processes the noisy speech data in a series of individual data units called “windows” that each includes sub-units called “frames”.
The feature extractor responsively filters the received noisy speech into a predetermined number of frequency sub-bands or channels using a filter bank to thereby generate filtered channel energy to a noise suppressor. The filtered channel energy is therefore preferably comprised of a series of discrete channels which the noise suppressor operates on concurrently.
Next, a noise calculator in the noise suppressor preferably calculates channel background noise values for each channel of the filter bank, and responsively stores the channel background noise values into a memory device. Similarly, a speech energy calculator in the noise suppressor preferably calculates speech energy values for each channel of the filter bank, and responsively stores the speech energy values into the memory device.
Then, a weighting module in the noise suppressor advantageously calculates individual weighting values for each calculated channel energy value. In a first embodiment, the weighting module calculates weighting values whose various channel values are related to the reciprocal of a channel average background noise variance value for the corresponding channel.
In a second embodiment, in order to reduce the dynamic range of the weighting procedure, the weighting module may calculate the individual weighting values as being equal to the reciprocal of a minimum variance of channel background noise for the corresponding channel. The weighting module therefore generates a total noise-suppressed channel energy that is the summation of each channel's channel energy value multiplied by that channel's calculated weighting value.
An endpoint detector then receives the noise-suppressed channel energy, and responsively detects corresponding speech endpoints. Finally, a recognizer receives the speech endpoints from the endpoint detector, and also receives feature vectors from the feature extractor, and responsively generates a recognition result using the endpoints and the feature vectors between the endpoints. The present invention thus efficiently and effectively implements a noise suppressor in a speech recognition system.
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Menendez-Pidal Xavier
Tanaka Miyuki
Wu Duanpei
Dorvil Richemond
Koerner Gregory J.
Simon & Koerner LLP
Storm Donald L.
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