Standard Automatic Speech Recognition Research Articles

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Unsupervised Adaptation of Categorical Prosody Models for Prosody Labeling and Speech Recognition

Automatic speech recognition (ASR) systems rely almost exclusively on short-term segment-level features (MFCCs), while ignoring higher level suprasegmental cues that are characteristic of human speech. However, recent experiments have shown that categorical representations of prosody, such as those based on the Tones and Break Indices (ToBI) annotation standard, can be used to enhance speech recognizers. However, categorical prosody models are severely limited in scope and coverage due to the lack of large corpora annotated with the relevant prosodic symbols (such as pitch accent, word prominence, and boundary tone labels). In this paper, we first present an architecture for augmenting a standard ASR with symbolic prosody. We then discuss two novel, un-supervised adaptation techniques for improving, respectively, the quality of the linguistic and acoustic components of our categorical prosody models. Finally, we implement the augmented ASR by enriching ASR lattices with the adapted categorical prosody models. Our experiments show that the proposed unsupervised adaptation techniques significantly improve the quality of the prosody models; the adapted prosodic language and acoustic models reduce binary pitch accent (presence versus absence) classification error rate by 13.8% and 4.3%, respectively (relative to the seed models) on the Boston University Radio News Corpus, while the prosody-enriched ASR exhibits a 3.1% relative reduction in word error rate (WER) over the baseline system.

Canonicalization of Feature Parameters for Robust Speech Recognition Based on Distinctive Phonetic Feature (DPF) Vectors

This paper describes a robust automatic speech recognition (ASR) system with less computation. Acoustic models of a hidden Markov model (HMM)-based classifier include various types of hidden factors such as speaker-specific characteristics, coarticulation, and an acoustic environment, etc. If there exists a canonicalization process that can recover the degraded margin of acoustic likelihoods between correct phonemes and other ones caused by hidden factors, the robustness of ASR systems can be improved. In this paper, we introduce a canonicalization method that is composed of multiple distinctive phonetic feature (DPF) extractors corresponding to each hidden factor canonicalization, and a DPF selector which selects an optimum DPF vector as an input of the HMM-based classifier. The proposed method resolves gender factors and speaker variability, and eliminates noise factors by applying the canonicalzation based on the DPF extractors and two-stage Wiener filtering. In the experiment on AURORA-2J, the proposed method provides higher word accuracy under clean training and significant improvement of word accuracy in low signal-to-noise ratio (SNR) under multi-condition training compared to a standard ASR system with mel frequency ceptral coeffient (MFCC) parameters. Moreover, the proposed method requires a reduced, two-fifth, Gaussian mixture components and less memory to achieve accurate ASR.

Talking to Machines: Introducing Robot Perception to Resolve Speech Recognition Uncertainties

The use of spontaneous speech as a form of communication between humans and robots is a potential solution for more efficient human-robot interactions. Accuracy is one of the main problems associated with the automatic speech recognition (ASR) component of human-robot interactive systems. The standard ASR approach is based on statistical methods applied to phoneme domains. However, some problems cannot be solved with the rule-based approaches used so far; therefore, alternative strategies could be the solution. The aim of this paper is to investigate some aspects related to the use of a robot's perceptive abilities to increase the robustness of ASR components. The robot evaluative abilities are used to incrementally build knowledge that will be used during the recognition phase. This paper covers aspects concerning the use of time-warping algorithms to improve the speech recognition performance. In particular, aspects related to the accuracy and efficiency of this approach when applied to whole-sentence speech signals are discussed.

Speech encoding in a model of peripheral auditory processing: Quantitative assessment by means of automatic speech recognition

Our notion of how speech is processed is still very much dominated by von Helmholtz’s theory of hearing. He deduced that the human inner ear decomposes the spectrum of sound signals. However, physiological recordings of auditory nerve fibers (ANF) showed that the rate-place code, which is thought to transmit spectral information to the brain, is at least complemented by a temporal code. In our paper we challenge the rate-place code using a complex but realistic scenario: speech in noise. We used a detailed model of human auditory processing that closely replicates key aspects of auditory nerve spike trains. We performed quantitative evaluations of coding strategies using standard automatic speech recognition (ASR) tools. Our test data was spoken letters of the whole English alphabet from a variety of speakers, with and without background noise. We evaluated a purely rate-place-based encoding strategy, a temporal strategy based on interspike intervals, and a combination thereof. The results suggest that as few as 4% of the total number of ANFs would be sufficient to code speech information in a rate-place fashion. Rate-place coding performed its best for speech in clean conditions at normal sound level, but broke down at higher-than-normal levels, and failed dramatically in noise at high levels. Low-spontaneous rate fibers improved the rate-place code, mainly for vowels and at higher-than-normal levels. At high speech levels, and in particular in the presence of background noise, combining rate-place coding with the temporal coding strategy greatly improved recognition accuracy. We therefore conclude that the human auditory system does not rely on a rate-place code alone but requires the abundance of fibers for precise temporal coding.

Noise-tolerant speech recognition: the SNN-TA approach

Neural network learning theory draws a relationship between “learning with noise” and applying a regularization term in the cost function that is minimized during the training process on clean (non-noisy) data. Application of regularizers and other robust training techniques are aimed at improving the generalization capabilities of connectionist models, reducing overfitting. In spite of that, the generalization problem is usually overlooked by automatic speech recognition (ASR) practioners who use hidden Markov models (HMM) or other standard ASR paradigms. Nonetheless, it is reasonable to expect that an adequate neural network model (due to its universal approximation property and generalization capability) along with a suitable regularizer can exhibit good recognition performance whenever noise is added to the test data, although training is accomplished on clean data. This paper presents applications of a variant of the so called segmental neural network (SNN), introduced at BBN by Zavaliagkos et al. for rescoring the N-best hypothesis yielded by a standard continuous density HMM (CDHMM). An enhanced connectionist model, called SNN with trainable amplitude of activation functions (SNN-TA) is first used in this paper instead of the CDHMM to perform the recognition of isolated words. Viterbi-based segmentation is then introduced, relying on the level-building algorithm, that can be combined with the SNN-TA to obtain a hybrid framework for continuous speech recognition. The proposed paradigm is applied to the recognition of isolated and connected Italian digits under several noisy conditions, outperforming the CDHMMs.

Comparison of standard ASR front ends and auditory models in neural net-based automatic speech recognition

Recent work [Renals and Terry, European Conference on Speech Technology, Edinburgh, United Kingdom (1987)] reported on a small multispeaker isolated digit automatic speech recognition (ASR) experiment using a backward propagation neural net system with a synchrony auditory model as its front end. In spite of fairly crude temporal normalization, the system was capable of better than 80% ASR accuracy. The question remains if the reported performance is due to (a) the paticular front end, (b) the particular neural net-based classification, (c) the particular time-normalization scheme, or (d) the combination of all factors. The goal here is to isolate these factors. In the present contribution, different front ends in the neural net-based ASR are systematically evaluated. Several standard ASR front ends are compared with the synchrony auditory model and the perceptually based linear predictive auditory model front ends in both the speaker-dependent and the speaker-independent ASR. The speed of learning and the ASR accuracy of the compared recognizers are reported and discussed.