Speech Recognition In Adverse Environments Research Articles

A comparison of front-ends for bitstream-based ASR over IP

Automatic speech recognition (ASR) is called to play a relevant role in the provision of spoken interfaces for IP-based applications. However, as a consequence of the transit of the speech signal over these particular networks, ASR systems need to face two new challenges: the impoverishment of the speech quality due to the compression needed to fit the channel capacity and the inevitable occurrence of packet losses. In this framework, bitstream-based approaches that obtain the ASR feature vectors directly from the coded bitstream, avoiding the speech decoding process, have been proposed ([S.H. Choi, H.K. Kim, H.S. Lee, Speech recognition using quantized LSP parameters and their transformations in digital communications, Speech Commun. 30 (4) (2000) 223–233. A. Gallardo-Antolín, C. Pelàez-Moreno, F. Díaz-de-María, Recognizing GSM digital speech, IEEE Trans. Speech Audio Process., to appear. H.K. Kim, R.V. Cox, R.C. Rose, Performance improvement of a bitstream-based front-end for wireless speech recognition in adverse environments, IEEE Trans. Speech Audio Process. 10 (8) (2002) 591–604. C. Peláez-Moreno, A. Gallardo-Antolín, F. Díaz-de-María, Recognizing voice over IP networks: a robust front-end for speech recognition on the WWW, IEEE Trans. Multimedia 3(2) (2001) 209–218], among others) to improve the robustness of ASR systems. LSP (Line Spectral Pairs) are the preferred set of parameters for the description of the speech spectral envelope in most of the modern speech coders. Nevertheless, LSP have proved to be unsuitable for ASR, and they must be transformed into cepstrum-type parameters. In this paper we comparatively evaluate the robustness of the most significant LSP to cepstrum transformations in a simulated VoIP (voice over IP) environment which includes two of the most popular codecs used in that network (G.723.1 and G.729) and several network conditions. In particular, we compare ‘pseudocepstrum’ [H.K. Kim, S.H. Choi, H.S. Lee, On approximating Line Spectral Frequencies to LPC cepstral coefficients, IEEE Trans. Speech Audio Process. 8 (2) (2000) 195–199], an approximated but straightforward transformation of LSP into LP cepstral coefficients, with a more computationally demanding but exact one. Our results show that pseudocepstrum is preferable when network conditions are good or computational resources low, while the exact procedure is recommended when network conditions become more adverse.

A method of joint compensation of additive and convolutive distortions for speaker-independent speech recognition

A speech recognizer operating in a mobile environment has to be robust to two distortion sources: ambient noise (additive distortion) and microphone changes (convolutive distortion). Explicitly and simultaneously modeling the two distortion sources has been a great challenge for speech recognition in adverse environments. In this paper, two log-spectral domain components are introduced in speech acoustic models to represent additive and convolutive distortions. A method, called JAC, jointly compensates both additive and convolutive distortions. For each utterance to be recognized, it adapts HMM mean vectors with a noise estimate and a channel estimate. The noise estimate is calculated from the pre-utterance pause and the channel estimate is calculated using an EM algorithm from speech utterances produced in the distortion environment. The algorithm is evaluated on a noisy speech database recorded in-vehicle with a hands-free distant microphone in several sessions, including parked, stop-and-go, and highway driving conditions. Experiments show that the method typically reduces recognition word error rate by an order of magnitude. The method makes it possible to obtain high performance for speaker-independent recognition in changing noisy environments without collecting any noisy speech for training.

Auditory analysis for speech recognition based on physiological models

To address the limitations of traditional cepstrum or LPC based front-end processing methods for automatic speech recognition, more elaborate methods based on physiological models of the human auditory system may be used to achieve more robust speech recognition in adverse environments. For this purpose, a modified version of a model of the primary auditory cortex featuring a three dimensional mapping of auditory spectra [Wang and Shamma, IEEE Trans. Speech Audio Process. 3, 382–395 (1995)] is adopted and investigated for its use as an improved front-end processing method. The study is conducted in two ways: first, by relating the model’s redundant representation to traditional spectral representations and showing that the former not only encompasses information provided by the latter, but also reveals more relevant information that makes it superior in describing the identifying features of speech signals; and second, by observing the statistical features of the representation for various classes of sound to show how different identifying features manifest themselves as specific patterns on the cortical map, thereby becoming a place-coded data set on which detection theory could be applied to simulate auditory perception and cognition.

Performance improvement of a bitstream-based front-end for wireless speech recognition in adverse environments

We propose a feature enhancement algorithm for wireless speech recognition in adverse acoustic environments. A speech recognition system is realized at the network side of a wireless communications system and feature parameters are extracted directly from the bitstream of the speech coder employed in the system, where the feature parameters are composed of spectral envelope information and coder-specific information. The coder-specific information is apt to be affected by environmental noise because the speech coder fails to generate high quality speech in noisy environments. We first found that enhancing noisy speech prior to speech coding improves the recognizer's performance. However, our aim was to develop a robust front-end operating at the network side of a wireless communications system without regard to whether speech enhancement was applied at the sender side. We investigated the effect of a speech enhancement algorithm on the bitstream-based feature parameters. Consequently, a feature enhancement algorithm is proposed which incorporates feature parameters obtained from the decoded speech and a noise suppressed version of the decoded speech. The coder-specific information can also be improved by re-estimating the codebook gains and residual energy from the enhanced residual signal. HMM-based connected digit recognition experiments show that the proposed feature enhancement algorithm significantly improves recognition performance at low signal-to-noise ratio (SNR) without causing poorer performance at high SNR. From large vocabulary speech recognition experiments with far-field microphone speech signals recorded in an office environment, we show that the feature enhancement algorithm greatly improves word recognition accuracy.

Robust speech recognition in adverse environments by separating speech and noise sources using JADE-ICA

Spoken dialogue information retrieval applications are the future trend for mobile users. The typical presence of background noise in mobile environments causes significant reduction in speech recognition accuracy unless the recognizers are trained explicitly for each environment. However, it is not practical to train recognizers for every environment. One solution to this problem is to use an array of microphones and then separate the speech signals from noise by applying Blind Source Separation (BSS) techniques. BSS based on Independent Component Analysis (ICA) is one of the efficient techniques to separate mixed signals and JADE-ICA is one of ICA’s variants that is applicable for non-Gaussian signals. The JADE-ICA is applied here, since generally speech and noise are non-Gaussian signals. An array of two microphones was considered and two signal sources: speech and in-vehicle noise were assumed. Recognition accuracy experiments were conducted on 1831 utterances from 1831 speakers after separating the speech signals from noise using the JADE-ICA. The word recognition accuracy for three conditions: clean speech, noisy speech (SNR = 0 dB) and noise separated speech are 91.9%, 45.2% and 91.9%, respectively. This indicates that using the JADE-ICA to separate speech from noise results in the same recognition accuracy as that of clean speech. The details on JADE-ICA and simulations will be provided in the full paper.

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

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.

Automatic Speech Recognition in Adverse Environments

Technological developments in automatic speech recognition (ASR) technology over the last couple of decades have resulted in ASR being considered for use in an ever-increasing number of applications. However, a number of physical and psychological environments are still deemed inappropriate for this technology. Several of the stressors creating adverse environments are considered here, in conjunction with their effects on human speech production, human performance in general, and the implications for ASR system design. We propose that a combination of continuing technological developments, together with the application of system engineering and human factors, will lead to ensuring the attainment of robust ASR performance in a number of environments previously considered as high risk.

Comparison of some noise-compensation methods for speech recognition in adverse environments

A comparative study is presented of three noise-compensation schemes, namely spectral subtraction, Wiener filters, and noise adaptation, for hidden-Markov-model-based speech recognition in adverse environments. The noise-compensation methods are evaluated on a spoken-digit database, in the presence of car noise and helicopter noise at different signal-to-noise ratios. Experimental results demonstrate that the noise-compensation methods achieve a substantial improvement in recognition accuracy across a wide range of signal-to-noise ratios. At a signal-to-noise ratio of -6 dB the recognition accuracy is improved from 11% to 83%. The use of cepstral-time matrices as an improved speech representation is also considered, and their combination with the noise-compensation methods is shown. Experiments show that the cepstral-time matrix is a more robust feature than a vector of identical size, composed of a combination of cepstral and differential cepstral features.

Speech recognition in adverse environments

The problem of automatic speech recognition in an adverse environment has attracted the attention of many researchers. The main reason is that the performance of existing speech recognition systems, whose designs are predicated on assumptions about the environment conditions, such as low noise or low interference, degrades rapidly in the presence of noise and distortion. In this paper, we review several promising methods that were proposed in the past few years to deal with this problem. We discuss methods or algorithms in six categories: signal enhancement preprocessing; special transducer arrangements; noise masking; stress compensation; robust distortion measures; and novel speech representations. We explain each type of approach and provide a summary of the performance improvements each method is able to achieve. This type of information is helpful in making a technical decision for the actual recognizer design to be deployed in adverse environments.