Synthesis using speaker adaptation from speech recognition DB

This paper deals with the creation of multiple voices from a Hidden Markov Model based speech synthesis system (HTS). More than 150 Catalan synthetic voices were built using Hidden Markov Models (HMM) and speaker adaptation techniques. Training data for building a Speaker-Independent (SI) model were selected from both a general purpose speech synthesis database (FestCat;) and a database design ed for training Automatic Speech Recognition (ASR) systems (Catalan SpeeCon database). The SpeeCon database was also used to adapt the SI model to different speakers. Using an ASR designed database for TTS purposes provided many different amateur voices, with few minutes of recordings not performed in studio conditions. This paper shows how speaker adaptation techniques provide the right tools to generate multiple voices with very few adaptation data. A subjective evaluation was carried out to assess the intelligibility and naturalness of the generated voices as well as the similarity of the adapted voices to both the original speaker and the average voice from the SI model.Peer ReviewedPostprint (published version

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

Kurdish Dialects and Neighbor Languages Automatic Recognition

Dialect recognition is one of the most hot topics in the speech analysis area. In this study a system for dialect and language recognition is developed using phonetic and a style based features. The study suggests a new set of feature using one-dimensional LBP feature.  The results show that the proposed LBP set of feature is useful to improve dialect and language recognition accuracy. The acquired data involved in this study are three Kurdish dialects (Sorani, Badini and Hawrami) with three neighbor languages (Arabic, Persian and Turkish). The study proposed a new method to interpret the closeness of the Kurdish dialects and their neighbor languages using confusion matrix and a non-metric multi-dimensional visualization technique. The result shows that the Kurdish dialects can be clustered and linearly separated from the neighbor languages

Clearing the transcription hurdle in dialect corpus building : the corpus of Southern Dutch dialects as case-study

This paper discusses how the transcription hurdle in dialect corpus building can be cleared. While corpus analysis has strongly gained in popularity in linguistic research, dialect corpora are still relatively scarce. This scarcity can be attributed to several factors, one of which is the challenging nature of transcribing dialects, given a lack of both orthographic norms for many dialects and speech technological tools trained on dialect data. This paper addresses the questions (i) how dialects can be transcribed efficiently and (ii) whether speech technological tools can lighten the transcription work. These questions are tackled using the Southern Dutch dialects (SDDs) as case study, for which the usefulness of automatic speech recognition (ASR), respeaking, and forced alignment is considered. Tests with these tools indicate that dialects still constitute a major speech technological challenge. In the case of the SDDs, the decision was made to use speech technology only for the word-level segmentation of the audio files, as the transcription itself could not be sped up by ASR tools. The discussion does however indicate that the usefulness of ASR and other related tools for a dialect corpus project is strongly determined by the sound quality of the dialect recordings, the availability of statistical dialect-specific models, the degree of linguistic differentiation between the dialects and the standard language, and the goals the transcripts have to serve

Afrikaans and Dutch as closely-related languages: A comparison to West Germanic languages and Dutch dialects

Following Den Besten‟s (2009) desiderata for historical linguistics of Afrikaans, this article aims to contribute some modern evidence to the debate regarding the founding dialects of Afrikaans. From an applied perspective (i.e. human language technology), we aim to determine which West Germanic language(s) and/or dialect(s)  would be best suited for the purposes of recycling speech resources for the benefit of developing speech  technologies for Afrikaans. Being recognised as a West Germanic language, Afrikaans is first compared to  Standard Dutch, Standard Frisian and Standard German. Pronunciation distances are measured by means of  Levenshtein distances. Afrikaans is found to be closest to Standard Dutch. Secondly, Afrikaans is compared to 361 Dutch dialectal varieties in the Netherlands and North-Belgium, using material from the Reeks  Nederlandse Dialectatlassen, a series of dialect atlases compiled by Blancquaert and Pée in the period  1925-1982 which cover the Dutch dialect area. Afrikaans is found to be closest to the South-Holland dialectal variety of Zoetermeer; this largely agrees with the findings of Kloeke (1950). No speech resources are  available for Zoetermeer, but such resources are available for Standard Dutch. Although the dialect of  Zoetermeer is significantly closer to Afrikaans than Standard Dutch is, Standard Dutch speech resources might be a good substitute.Keywords: human language technologies, speech resources, Afrikaans, Dutch, acoustic distanc