683 research outputs found
Improvements on automatic speech segmentation at the phonetic level
In this paper, we present some recent improvements in our automatic speech segmentation system, which only needs the speech signal and the phonetic sequence of each sentence of a corpus to be trained. It estimates a GMM by using all the sentences of the training subcorpus, where each Gaussian distribution represents an acoustic class, which probability densities are combined with a set of conditional probabilities in order to estimate the probability densities of the states of each phonetic unit. The initial values of the conditional probabilities are obtained by using a segmentation of each sentence assigning the same number of frames to each phonetic unit. A DTW algorithm fixes the phonetic boundaries using the known phonetic sequence. This DTW is a step inside an iterative process which aims to segment the corpus and re-estimate the conditional probabilities. The results presented here demonstrate that the system has a good capacity to learn how to identify the phonetic boundaries. © 2011 Springer-Verlag.This work was supported by the Spanish MICINN under
contract TIN2008-06856-C05-02Gómez Adrian, JA.; Calvo Lance, M. (2011). Improvements on automatic speech segmentation at the phonetic level. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 7042:557-564. https://doi.org/10.1007/978-3-642-25085-9_66S5575647042Toledano, D.T., Hernández Gómez, L., Villarrubia Grande, L.: Automatic Phonetic Segmentation. IEEE Transactions on Speech and Audio Processing 11(6), 617–625 (2003)Kipp, A., Wesenick, M.B., Schiel, F.: Pronunciation modelling applied to automatic segmentation of spontaneous speech. In: Proceedings of Eurospeech, Rhodes, Greece, pp. 2013–2026 (1997)Sethy, A., Narayanan, S.: Refined Speech Segmentation for Concatenative Speech Synthesis. In: Proceedings of ICSLP, Denver, Colorado, USA, pp. 149–152 (2002)Jarify, S., Pastor, D., Rosec, O.: Cooperation between global and local methods for the automatic segmentation of speech synthesis corpora. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 1666–1669 (2006)Romsdorfer, H., Pfister, B.: Phonetic Labeling and Segmentation of Mixed-Lingual Prosody Databases. In: Proceedings of Interspeech, Lisbon, Portual, pp. 3281–3284 (2005)Paulo, S., Oliveira, L.C.: DTW-based Phonetic Alignment Using Multiple Acoustic Features. In: Proceedings of Eurospeech, Geneva, Switzerland, pp. 309–312 (2003)Park, S.S., Shin, J.W., Kim, N.S.: Automatic Speech Segmentation with Multiple Statistical Models. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 2066–2069 (2006)Mporas, I., Ganchev, T., Fakotakis, N.: Speech segmentation using regression fusion of boundary predictions. Computer Speech and Language 24, 273–288 (2010)Povey, D., Woodland, P.C.: Minimum Phone Error and I-smoothing for improved discriminative training. In: Proceedings of ICASSP, Orlando, Florida, USA, pp. 105–108 (2002)Kuo, J.W., Wang, H.M.: Minimum Boundary Error Training for Automatic Phonetic Segmentation. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 1217–1220 (2006)Huggins-Daines, D., Rudnicky, A.I.: A Constrained Baum-Welch Algorithm for Improved Phoneme Segmentation and Efficient Training. In: Proceedings of Interspeech, Pittsburgh, Pennsylvania, USA, pp. 1205–1208 (2006)Ogbureke, K.U., Carson-Berndsen, J.: Improving initial boundary estimation for HMM-based automatic phonetic segmentation. In: Proceedings of Interspeech, Brighton, UK, pp. 884–887 (2009)Gómez, J.A., Castro, M.J.: Automatic Segmentation of Speech at the Phonetic Level. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 672–680. Springer, Heidelberg (2002)Gómez, J.A., Sanchis, E., Castro-Bleda, M.J.: Automatic Speech Segmentation Based on Acoustical Clustering. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 540–548. Springer, Heidelberg (2010)Moreno, A., Poch, D., Bonafonte, A., Lleida, E., Llisterri, J., Mariño, J.B., Nadeu, C.: Albayzin Speech Database: Design of the Phonetic Corpus. In: Proceedings of Eurospeech, Berlin, Germany, vol. 1, pp. 653–656 (September 1993)TIMIT Acoustic-Phonetic Continuous Speech Corpus, National Institute of Standards and Technology Speech Disc 1-1.1, NTIS Order No. PB91-5050651996 (October 1990
A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.5 page(s
Automatic Speech Segmentation Based on HMM
This contribution deals with the problem of automatic phoneme segmentation using HMMs. Automatization of speech segmentation task is important for applications, where large amount of data is needed to process, so manual segmentation is out of the question. In this paper we focus on automatic segmentation of recordings, which will be used for triphone synthesis unit database creation. For speech synthesis, the speech unit quality is a crucial aspect, so the maximal accuracy in segmentation is needed here. In this work, different kinds of HMMs with various parameters have been trained and their usefulness for automatic segmentation is discussed. At the end of this work, some segmentation accuracy tests of all models are presented
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
Mandarin Singing Voice Synthesis Based on Harmonic Plus Noise Model and Singing Expression Analysis
The purpose of this study is to investigate how humans interpret musical
scores expressively, and then design machines that sing like humans. We
consider six factors that have a strong influence on the expression of human
singing. The factors are related to the acoustic, phonetic, and musical
features of a real singing signal. Given real singing voices recorded following
the MIDI scores and lyrics, our analysis module can extract the expression
parameters from the real singing signals semi-automatically. The expression
parameters are used to control the singing voice synthesis (SVS) system for
Mandarin Chinese, which is based on the harmonic plus noise model (HNM). The
results of perceptual experiments show that integrating the expression factors
into the SVS system yields a notable improvement in perceptual naturalness,
clearness, and expressiveness. By one-to-one mapping of the real singing signal
and expression controls to the synthesizer, our SVS system can simulate the
interpretation of a real singer with the timbre of a speaker.Comment: 8 pages, technical repor
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