Comparing timing models of two Swiss German dialects

Research on dialectal varieties was for a long time concentrated on phonetic aspects of language. While there was a lot of work done on segmental aspects, suprasegmentals remained unexploited until the last few years, despite the fact that prosody was remarked as a salient aspect of dialectal variants by linguists and by naive speakers. Actual research on dialectal prosody in the German speaking area often deals with discourse analytic methods, correlating intonations curves with communicative functions (P. Auer et al. 2000, P. Gilles & R. Schrambke 2000, R. Kehrein & S. Rabanus 2001). The project I present here has another focus. It looks at general prosodic aspects, abstracted from actual situations. These global structures are modelled and integrated in a speech synthesis system. Today, mostly intonation is being investigated. However, rhythm, the temporal organisation of speech, is not a core of actual research on prosody. But there is evidence that temporal organisation is one of the main structuring elements of speech (B. Zellner 1998, B. Zellner Keller 2002). Following this approach developed for speech synthesis, I will present the modelling of the timing of two Swiss German dialects (Bernese and Zurich dialect) that are considered quite different on the prosodic level. These models are part of the project on the "development of basic knowledge for research on Swiss German prosody by means of speech synthesis modelling" founded by the Swiss National Science Foundation

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

A Timing Model for Fast French

Models of speech timing are of both fundamental and applied interest. At the fundamental level, the prediction of time periods occupied by syllables and segments is required for general models of speech prosody and segmental structure. At the applied level, complete models of timing are an essential component of any speech synthesis system. Previous research has established that a large number of factors influence various levels of speech timing. Statistical analysis and modelling can identify order of importance and mutual influences between such factors. In the present study, a three-tiered model was created by a modified step-wise statistical procedure. It predicts the temporal structure of French, as produced by a single, highly fluent speaker at a fast speech rate (100 phonologically balanced sentences, hand-scored in the acoustic signal). The first tier models segmental influences due to phoneme type and contextual interactions between phoneme types. The second tier models syllable-level influences of lexical vs. grammatical status of the containing word, presence of schwa and the position within the word. The third tier models utterance-final lengthening. The complete segmental-syllabic model correlated with the original corpus of 1204 syllables at an overall r = 0.846. Residuals were normally distributed. An examination of subsets of the data set revealed some variation in the closeness of fit of the model. The results are considered to be useful for an initial timing model, particularly in a speech synthesis context. However, further research is required to extend the model to other speech rates and to examine inter-speaker variability in greater detail

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

Rhythmic unit extraction and modelling for automatic language identification

International audienceThis paper deals with an approach to Automatic Language Identification based on rhythmic modelling. Beside phonetics and phonotactics, rhythm is actually one of the most promising features to be considered for language identification, even if its extraction and modelling are not a straightforward issue. Actually, one of the main problems to address is what to model. In this paper, an algorithm of rhythm extraction is described: using a vowel detection algorithm, rhythmic units related to syllables are segmented. Several parameters are extracted (consonantal and vowel duration, cluster complexity) and modelled with a Gaussian Mixture. Experiments are performed on read speech for 7 languages (English, French, German, Italian, Japanese, Mandarin and Spanish) and results reach up to 86 ± 6% of correct discrimination between stress-timed mora-timed and syllable-timed classes of languages, and to 67 ± 8% percent of correct language identification on average for the 7 languages with utterances of 21 seconds. These results are commented and compared with those obtained with a standard acoustic Gaussian mixture modelling approach (88 ± 5% of correct identification for the 7-languages identification task)

Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC

The Resonant Dynamics of Speech Perception: Interword Integration and Duration-Dependent Backward Effects

How do listeners integrate temporally distributed phonemic information into coherent representations of syllables and words? During fluent speech perception, variations in the durations of speech sounds and silent pauses can produce different pereeived groupings. For exarnple, increasing the silence interval between the words "gray chip" may result in the percept "great chip", whereas increasing the duration of fricative noise in "chip" may alter the percept to "great ship" (Repp et al., 1978). The ARTWORD neural model quantitatively simulates such context-sensitive speech data. In AHTWORD, sequential activation and storage of phonemic items in working memory provides bottom-up input to unitized representations, or list chunks, that group together sequences of items of variable length. The list chunks compete with each other as they dynamically integrate this bottom-up information. The winning groupings feed back to provide top-down supportto their phonemic items. Feedback establishes a resonance which temporarily boosts the activation levels of selected items and chunks, thereby creating an emergent conscious percept. Because the resonance evolves more slowly than wotking memory activation, it can be influenced by information presented after relatively long intervening silence intervals. The same phonemic input can hereby yield different groupings depending on its arrival time. Processes of resonant transfer and competitive teaming help determine which groupings win the competition. Habituating levels of neurotransmitter along the pathways that sustain the resonant feedback lead to a resonant collapsee that permits the formation of subsequent. resonances.Air Force Office of Scientific Research (F49620-92-J-0225); Defense Advanced Research projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-92-J-1309, NOOO14-95-1-0657

Production and perception of speaker-specific phonetic detail at word boundaries

Experiments show that learning about familiar voices affects speech processing in many tasks. However, most studies focus on isolated phonemes or words and do not explore which phonetic properties are learned about or retained in memory. This work investigated inter-speaker phonetic variation involving word boundaries, and its perceptual consequences. A production experiment found significant variation in the extent to which speakers used a number of acoustic properties to distinguish junctural minimal pairs e.g. 'So he diced them'—'So he'd iced them'. A perception experiment then tested intelligibility in noise of the junctural minimal pairs before and after familiarisation with a particular voice. Subjects who heard the same voice during testing as during the familiarisation period showed significantly more improvement in identification of words and syllable constituents around word boundaries than those who heard different voices. These data support the view that perceptual learning about the particular pronunciations associated with individual speakers helps listeners to identify syllabic structure and the location of word boundaries