Speech recognition in noisy environments: a survey

Speech recognition in adverse environments

The authors use an enhanced analysis feature set consisting of both instantaneous and transitional spectral information and test the hidden-Markov-model (HMM)-based connected-digit recognizer in speaker-trained, multispeaker, and speaker-independent modes. For the evaluation, both a 50-talker connected-digit database recorded over local, dialed-up telephone lines, and the Texas Instruments, 225-adult-talker, connected-digits database are used. Using these databases, the performance achieved was 0.35, 1.65, and 1.75% string error rates for known-length strings, for speaker-trained, multispeaker, and speaker-independent modes, respectively, and 0.78, 2.85, and 2.94% string error rates for unknown-length strings of up to seven digits in length for the three modes. Several experiments were carried out to determine the best set of conditions (e.g., training, recognition, parameters, etc.) for recognition of digits. The results and the interpretation of these experiments are described. >

High performance connected digit recognition using hidden Markov models

An acoustical mismatch between the training and the testing conditions of hidden Markov model (HMM)-based speech recognition systems often causes a severte degradation in the recognition performance. In telephone speech recognition, for example, undesirable signal components due to ambient noise and channel distortion, as well as due to different variations of telephone handsets render the recognizer unusable for real- world applications. This paper presents a signal bias removal (SBR) method based on maximum likelihood1 estimation for the minimization of these undesirable effects. The proposed method is readily applicable in various architectures, i.e., dis- crete (vector-quantization based), semicontinuous and continuous density HMM. In this paper, the SBR method, integrated into a discrete density HMM, is applied to telephone speech recognition where the contamination due to extraneous signal components is assumed to be unknown. To enable real-time implementation, a sequential method for the estimation of the bias is presented. Experimental results for speaker-independent connected digit recognition show a reduction in the per digit error rate by up to 41% and 14% during mismatched and matclhed training and testing conditions, respectively.

Signal bias removal by maximum likelihood estimation for robust telephone speech recognition

Several noise compensation schemes for speech recognition in impulsive and nonimpulsive noise are considered. The noise compensation schemes are spectral subtraction, HMM-based Wiener (1949) filters, noise-adaptive HMMs, and a front-end impulsive noise removal. The use of the cepstral-time matrix as an improved speech feature set is explored, and the noise compensation methods are extended for use with cepstral-time features. Experimental evaluations, on a spoken digit database, in the presence of ear noise, helicopter noise, and impulsive noise, demonstrate that the noise compensation methods achieve substantial improvement in recognition across a wide range of signal-to-noise ratios. The results also show that the cepstral-time matrix is more robust than a vector of identical size, which is composed of a combination of cepstral and differential cepstral features.

Noise compensation methods for hidden Markov model speech recognition in adverse environments

Speech recognizers trained in quiet conditions but operating in noise usually have poor accuracy. This paper reports two methods for improving the accuracy of an LPC vector-quantization speech recognizer by adapting the vector codebook to noisy conditions. First, each codebook vector is changed to reflect the way people speak in noise. Second, the estimated spectrum of the background noise is added to the codebook vectors. These ideas have been tested on a total of 2400 utterances of digits recorded in a car by 4 speakers. A baseline word spotter similar to NTT's SPLIT system was modified by adapting its vector codebook to noise. This adapted codebook, when used with a new word decision criterion, yields error rates at least 4 times lower for noisy conditions. The accuracy is significantly better than without codebook adaptation techniques.

Speech recognition with a noise-adapting codebook

The authors describe a parallel frame-synchronous level-building algorithm, utilizing HMM word-models, for connected-speech recognition on a tree-structured parallel computer. The algorithm is scalable in the sense that the source code and execution time remain essentially the same as vocabulary size increases, so long as the hardware is scaled proportionally. An illustrative sizing and timing analysis of a speaker-independent connected-digit recognizer on the ASPEN tree-machine is described. This algorithm executes in real-time and achieves 98.3% string accuracy on the Texas Instruments digit data base. >

D.B. Roe

Papers

Speech recognition with a noise-adapting codebook

Parallel level-building on a tree machine (speech recognition)