Speech Recognition In Noisy Environments Research Articles

Speech recognition in noisy environments using first-order vector Taylor series

In this paper, we generalize relations between clean and noisy speech signal using vector Taylor series (VTS) expansion for noise-robust speech recognition. We use it for both the noisy data compensation and hidden Markov model (HMM) parameter adaptation, and apply it for the cepstral domain directly, while Moreno used it to estimate the log-spectral parameters. Also, we develop a detailed procedure to estimate environmental variables in the cepstral domain using the expectation and maximization (EM) algorithms based on the maximum likelihood (ML) sense. To evaluate the developed method, we conduct speaker-independent isolated word and continuous speech recognition experiments. White Gaussian and driving car noises added to clean speech at various SNR are used as disturbing sources. Using only noise statistics obtained from three frames of silence and noisy speech to be recognized, we achieve significant performance improvement. Especially, HMM parameter adaptation with VTS is more effective than the parallel model combination (PMC) based on the log-normal assumption.

Application of multi-layer perceptron in estimating speech/noise characteristics for speech recognition in noisy environment

In this paper, we will consider the problem of speech recognition under noisy conditions. Multi-layer perceptron (MLP) based estimators estimating filter bank channel outputs are tested on a speaker independent isolated digit recognition task in white noise conditions. Local statistical information of the speech and the noise are estimated online and used as inputs to the estimators. The results are comparable to those obtained in clean condition at a moderate signal-to-noise ratio (SNR) of 20 dB. Substantial improvement is also obtained at lower SNRs. By carefully studying the results, it is noted that the MLP based estimators appear to perform poorly when there is virtually no detectable significant speech activity. As a result, a modified gain function is introduced. This improves the performance even further.

Speech recognition in noisy environments: A survey

The performance levels of most current speech recognizers degrade significantly when environmental noise occurs during use. Such performance degradation is mainly caused by mismatches in training and operating environments. During recent years much effort has been directed to reducing this mismatch. This paper surveys research results in the area of digital techniques for single microphone noisy speech recognition classified in three categories: noise resistant features and similarity measurement, speech enhancement, and speech model compensation for noise. The survey indicates that the essential points in noisy speech recognition consist of incorporating time and frequency correlations, giving more importance to high SNR portions of speech in decision making, exploiting task-specific a priori knowledge both of speech and of noise, using class-dependent processing, and including auditory models in speech processing.

Speech recognition in noisy environments with the aid of microphone arrays

This paper presents a microphone array adaptive beamformer with a dual function. The noise enhanced output is suited to transmission as well as to use as input to speech recognition systems. The areas of use envisaged are the car. the factory floor and noisy offices. The underlying structure is a steered Griffiths-Jim beamformer, with an added speech detection switch for the selective adaptation of both sections. This beamformer is effective in suppressing both stationary and non-stationary interference and is therefore a preprocessor for a wider range of speech recognition applications than any single channel noise suppression scheme could handle. Experiments were performed in a reverberant room with a 4-microphone array. Typical SNR improvements for communication purposes range from 4 to 12 dB. The effective SNR improvement for speech recognition purposes ranges from 4 to 8 dB.