Modelling the effects of speech rate variation for automatic speech recognition

Wrede B. Modelling the effects of speech rate variation for automatic speech recognition. Bielefeld (Germany): Bielefeld University; 2002.In automatic speech recognition it is a widely observed phenomenon that variations in speech rate cause severe degradations of the speech recognition performance. This is due to the fact that standard stochastic based speech recognition systems specialise on average speech rate. Although many approaches to modelling speech rate variation have been made, an integrated approach in a substantial system still has be to developed. General approaches to rate modelling are based on rate dependent models which are trained with rate specific subsets of the training data. During decoding a signal based rate estimation is performed according to which the set of rate dependent models is selected. While such approaches are able to reduce the word error rate significantly, they suffer from shortcomings such as the reduction of training data and the expensive training and decoding procedure. However, phonetic investigations show that there is a systematic relationship between speech rate and the acoustic characteristics of speech. In fast speech a tendency of reduction can be observed which can be described in more detail as a centralisation effect and an increase in coarticulation. Centralisation means that the formant frequencies of vowels tend to shift towards the vowel space center while increased coarticulation denotes the tendency of the spectral features of a vowel to shift towards those of its phonemic neighbour. The goal of this work is to investigate the possibility to incorporate the knowledge of the systematic nature of the influence of speech rate variation on the acoustic features in speech rate modelling. In an acoustic-phonetic analysis of a large corpus of spontaneous speech it was shown that an increased degree of the two effects of centralisation and coarticulation can be found in fast speech. Several measures for these effects were developed and used in speech recognition experiments with rate dependent models. A thorough investigation of rate dependent models showed that with duration and coarticulation based measures significant increases of the performance could be achieved. It was shown that by the use of different measures the models were adapted either to centralisation or coarticulation. Further experiments showed that by a more detailed modelling with more rate classes a further improvement can be achieved. It was also observed that a general basis for the models is needed before rate adaptation can be performed. In a comparison to other sources of acoustic variation it was shown that the effects of speech rate are as severe as those of speaker variation and environmental noise. All these results show that for a more substantial system that models rate variations accurately it is necessary to focus on both, durational and spectral effects. The systematic nature of the effects indicates that a continuous modelling is possible

Text-based and Signal-based Prediction of Break Indices and Pause Durations

The relation between symbolic and signal features of prosodic boundaries is experimentally studied using prediction methods. Text-based break index prediction turns out to be fairly good, but signal-based prediction and pause duration prediction perform worse. A possible reason is that random signal feature variations, as usually produced by humans, are hard to predict

The Zero Resource Speech Challenge 2017

We describe a new challenge aimed at discovering subword and word units from raw speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It aims at constructing systems that generalize across languages and adapt to new speakers. The design features and evaluation metrics of the challenge are presented and the results of seventeen models are discussed.Comment: IEEE ASRU (Automatic Speech Recognition and Understanding) 2017. Okinawa, Japa

Multiple acoustic cues for Korean stops and automatic speech recognition

The purpose of this thesis is to analyse acoustic characteristics of Korean stops by way of multivariate statistical tests, and to apply the results of the analysis in Automatic Speech Recognition (ASR) of Korean. Three acoustic cues that differentiate three types of Ko¬ rean oral stops are closure duration, Voice Onset Time (VOT) and fundamental frequency (FO) of a vowel after a stop. We review the characteristics of these parameters previously reported in various phonetic studies and test their usefulness for differentiating the three types of stops on two databases, one with controlled contexts, as in other phonetic stud¬ ies, and the other a continuous speech database designed for ASR. Statistical tests on both databases confirm that the three types of stops can be differentiated by the three acoustic parameters. In order to exploit these parameters for ASR, a context dependent Hidden Markov Model (HMM) based baseline system with a short pause model is built, which results in great improvement of performance compared to other systems. For mod¬ elling of the three acoustic parameters, an automatic segmentation technique for closure and VOT is developed. Samples of each acoustic parameter are modelled with univariate and multivariate probability distribution functions. Stop probability from these models is integrated by a post-processing technique. Our results show that integration of stop prob¬ ability does not make much improvement over the results of a baseline system. However, the results suggest that stop probabilities will be useful in determining the correct hy¬ pothesis with a larger lexicon containing more minimal pairs of words that differ by the identity of just one stop

Extending automatic transcripts in a unified data representation towards a prosodic-based metadata annotation and evaluation

This paper describes a framework that extends automatic speech transcripts in order to accommodate relevant information coming from manual transcripts, the speech signal itself, and other resources, like lexica. The proposed framework automatically collects, relates, computes, and stores all relevant information together in a self-contained data source, making it possible to easily provide a wide range of interconnected information suitable for speech analysis, training, and evaluating a number of automatic speech processing tasks. The main goal of this framework is to integrate different linguistic and paralinguistic layers of knowledge for a more complete view of their representation and interactions in several domains and languages. The processing chain is composed of two main stages, where the first consists of integrating the relevant manual annotations in the speech recognition data, and the second consists of further enriching the previous output in order to accommodate prosodic information. The described framework has been used for the identification and analysis of structural metadata in automatic speech transcripts. Initially put to use for automatic detection of punctuation marks and for capitalization recovery from speech data, it has also been recently used for studying the characterization of disfluencies in speech. It was already applied to several domains of Portuguese corpora, and also to English and Spanish Broadcast News corpora