At&t Bell Laboratories

Lucent Technologies Inc.

Other

American Telephone & Telegraph Co., At&t Bell Laboratories

American Telephone and Telegraph, Co.

David Lynn Thomson

Rathinavelu Chengalvarayan

Anand Rangaswamy Setlur

Richard H Ketchum

Dimitrios P Prezas

Michael D Metcalf

Tracy Roberts

Hung Duong

Paul Laos

An Internet Access Device (IAD) is pre-configured with first, generic configuration information designating the address of a configuration server with which it will establish a communication session when the IAD is first powered up and connected to the Internet. The configuration server then downloads a second, specific configuration to the IAD, such as a hotline number designating a specific speech server, and creates a network data record to link a unique identifier for the IAD, such as a serial number, with a user account. When the IAD goes off-hook, the IAD connects directly to a speech server using the second configuration. The speech server provides a smart dial tone, such as a voice prompt. The speech server responds to a verbal request spoken by a user by performing a corresponding activity, such as placing a telephone call or providing voicemail access.

Remote configuration of a voice over internet protocol telephone for smart dial tone

A system and method provide ASR talkoff suppression in a prompt-and collect voice transaction system in which a prompt signal is sent and an input signal is received. A comparison is made of a characteristic of the prompt signal and the input signal. Processing of the input signal to reduce the likelihood of an ASR response thereto is performed if the result of the comparison satisfies a predetermined criterion.

ASR talkoff suppressor

An automatic speech recognition system for the condition that an incoming caller's speech is quiet and a resulting echo (of a loud playing prompt) can cause the residual (the portion of the echo remaining after even echo cancellation) to be of the magnitude of the incoming speech input. Such loud echoes can falsely trigger the speech recognition system and interfere with the recognition of valid input speech. An echo model has been proven to alleviate this fairly common problem and to be effective in eliminating such false triggering. Further, this automatic speech recognition system enhanced the recognition of valid speech was provided within an existing hidden Markov modeling framework.

HMM-based echo model for noise cancellation avoiding the problem of false triggers

A class of features related to voicing parameters that indicate whether the vocal chords are vibrating. Features describing voicing characteristics of speech signals are integrated with an existing 38-dimensional feature vector consisting of first and second order time derivatives of the frame energy and of the cepstral coefficients with their first and second derivatives. Hidden Markov Model (HMM)-based connected digit recognition experiments comparing the traditional and extended feature sets show that voicing features and spectral information are complementary and that improved speech recognition performance is obtained by combining the two sources of information.

Use of periodicity and jitter for automatic speech recognition

A harmonic coding arrangement where the magnitude spectrum of the input speech is modeled at the analyzer by a relatively small set of parameters and, significantly, as a continuous rather than only a line magnitude spectrum. The synthesizer, rather than the analyzer, determines the magnitude, frequency, and phase of a large number of sinusoids which are summed to generate synthetic speech. Rather than receiving information explicitly defining the sinusoids from the analyzer, the synthesizer receives the small set of parameters and uses those parameters to determine a spectrum, which, in turn, is used by the synthesizer to determine the sinusoids for synthesis.

Harmonic speech coding arrangement where a set of parameters for a

Apparatus for detecting a fundamental frequency in speech in a changing speech environment by using adaptive statistical techniques. A statistical voice detector detects changes in the voice environment by classifiers that define certain attributes of the speech to recalculate weights that are used to combine the classifiers in making the unvoiced/voiced decision that specifies whether the speech has a fundamental frequency or not. The detector is responsive to classifiers to first calculate the average of the classifiers and then to determine the overall probability that any frame will be unvoiced. In addition, the detector forms two vectors, one vector represents the statistical average of values that an unvoiced frame's classifiers would have and the other vector represents the statistical average of the values of the classifiers for a voiced frame. These latter calculations are performed utilizing not only the average value of the classifiers and present classifiers but also a vector defining the weights that are utilized to determine whether a frame is unvoiced or not plus a threshold value. A weights calculator is responsive to the information generated in the statistical calculation to generate a new set of values for the weights vector and the threshold value which are utilized by the statistical calculator during the next frame. An unvoiced/voiced determinator then is responsive to the two statistical average vectors and the weights vector to make the unvoiced/voiced decision.

Adaptive multivariate estimating apparatus

A harmonic speech coding arrangement where vector quantization is used to improve speech quality. Parameters are determined at the analyzer of an illustrative coding arrangement to model the magnitude and phase spectra of the input speech. A first codebook of vectors is searched for a vector that closely approximates the difference between the true and estimated magnitude spectra. A second codebook of vectors is searched for a vector that closely approximates the difference between the true and the estimated phase spectra. Indices and scaling factors for the vectors are communicated to the synthesizer such that scaled vectors can be added into the magnitude and phase spectra for use at the synthesizer in generating speech as a sum of sinusoids.

Vector quantization in a harmonic speech coding arrangement

Statistically analyzing a discriminant variable generated by a discriminant voiced detector is done to determine the presence of the fundamental frequency in a changing speech environment. The detector is responsive to the discriminant variable to first calculate the average of all of the values of the discriminant variable over the present and past speech frames and then to determine the overall probability that any frame will be unvoiced. In addition, the detector calculates two values, one value represents the statistical average of discriminant values that an unvoiced frame's discriminant variable would have and the other value represents the statistical average of the discriminant values for voice frames. These latter calculations are performed utilizing not only the average discriminant value but also a weight value and a threshold value which are adaptively determined from frame to frame. The unvoiced/voiced decision is made by utilizing the weight and threshold values.

Adaptive threshold voiced detector

Apparatus for detecting a fundamental frequency in speech utilizing a plurality of voiced detectors and selecting one of those detectors to make the voicing decision utilizing distance measurement values with each value generated by one of the voiced detectors. The voiced detector selected is the one which generated the best distance measurement value. The distance measurement value may be the Mahalanobis distance value or Hotelling's two-sample T.sup.2 statistic. Two types of voiced detectors are disclosed: statistical voiced detectors and discriminant voiced detectors. The disclosed statistical voiced detector adapts to changing speech environments by detecting changes in the voice environment in response to classifiers that define certain attributes of the speech.

Distance measurement control of a multiple detector system

A speech analysis and synthesis system where pitch information for excitation is transmitted during voice segments of speech and pulse excitation or noise excitation is transmitted during unvoiced speech segments along with linear predictive coding (LPC) parameters. The decision of whether to transmit noise excitation or pulse excitation is performed by comparing the variance of the residual to the square of the mean amplitude of the rectified residual for each frame. If the result of this comparison is greater than a threshold value, pulse excitation is utilized otherwise noise excitation is used. The pulse excitation comprises a subset of samples of the LPC residual as determined by the relative amplitudes and spacing of the local maxima in the LPC residual.

Voice synthesis utilizing multi-level filter excitation

Improved significant sample detection for a pitch detector for use with speech analysis and synthesis methods by performing a reverse order search and a forward order search of digitized speech samples. A reverse search detector is responsive to segmented digital samples for determining a set of candidate samples by initially selecting one of the digitized samples as a present candidate sample and comparing in reverse order each of the digitized samples with the present candidate sample until a digitized sample is found whose amplitude is greater than the present candidate sample or the compared sample is greater than a predefined number of samples from the present candidate sample. When either of the previous conditions occurs, the compared digital sample becomes the new present candidate sample and the reverse search continues. After the reverse search has been performed and a set of candidate samples has been determined, a forward search detector then initially determines a present significant sample. The latter detector compares this significant sample with each of the candidate samples until a candidate sample is found whose amplitude is greater than the present significant sample or the compared candidate sample is more than a predefined number of samples away from the present significant sample. When either of those conditions occurs, the forward search detector saves the value of the amplitude and location of the candidate sample and replaces the present significant sample with that candidate sample and continues the search.

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Lisle, IL, United States of America

David Lynn Thomson