Robust parameters for automatic segmentation of speech

Automatic segmentation of speech ir on important problem that is useful in speed recognition, synthesis and coding. We explore in this paper: the robust parameter set, weightingfunction and distance measure for reliable segmentation of noisy speech. It is found that the MFCC parometers, successful in speech recognition. holds the best promise far robust segmentation also. We also explored a variery of symmetric and asymmetric weighting lifter, from which it is found that a symmetric lifter of the form $1+{Asin^{1/2}}{(\pi n/L)}$, ${0}\leq{n}\leq{L-1}$, for MFCC dimension L, is most effective. With regard to distance measure. the direct $L_2$ norm is found adequate

Speech segmentation and clustering methods for a new speech recognition architecture

Perinteiset automaattiset puheentunnistusmenetelmät eivät pärjää suorituskyvyssä ihmisen puheenhavaintokyvylle. Voidaksemme kuroa tämän eron umpeen, on kehitettävä täysin uudentyyppisiä arkkitehtuureja puheentunnistusta varten. Puhetta ja kieltä itsestään ihmisen lailla oppiva järjestelmä on yksi tällainen vaihtoehto. Tämä diplomityö esittelee erään lähtökohdan oppivalle järjestelmälle, koostuen uudenlaisesta sokeasta puheen segmentointialgoritmista, segmenttien piirteistyksestä, sekä menetelmistä vähittäiselle puhedatan luokittelulle klusteroinnin avulla. Kaikki metodit arvioitiin kattavilla kokeilla, ja itse arviontimenetelmien luonteeseen kiinnitettiin huomiota. Segmentoinnissa saavutettiin alan kirjallisuuteen nähden hyvät tulokset. Järjestelmän mahdollisia jatkokehityssuuntauksia on hahmoteltu muunmuassa mahdollisten muistiarkkitehtuurien ja älykkään top-down palautteen osalta.To reduce the gap between performance of traditional speech recognition systems and human speech recognition skills, a new architecture is required. A system that is capable of incremental learning offers one such solution to this problem. This thesis introduces a bottom-up approach for such a speech processing system, consisting of a novel blind speech segmentation algorithm, a segmental feature extraction methodology, and data classification by incremental clustering. All methods were evaluated by extensive experiments with a broad range of test material and the evaluation methodology was itself also scrutinized. The segmentation algorithm achieved above standard quality results compared to what is found in current literature regarding blind segmentation. Possibilities for follow-up research of memory structures and intelligent top-down feedback in speech processing are also outlined

Robust Parameters for Automatic Segmentation of Speech

Automatic segmentation of speech ir on important problem that is useful in speed recognition, synthesis and coding. We explore in this paper: the robust parameter set, weightingfunction and distance measure for reliable segmentation of noisy speech. It is found that the MFCC parometers, successful in speech recognition. holds the best promise far robust segmentation also. We also explored a variery of symmetric and asymmetric weighting lifter, from which it is found that a symmetric lifter of the form $1+{Asin^{1/2}}{(\pi n/L)}$, ${0}\leq{n}\leq{L-1}$, for MFCC dimension L, is most effective. With regard to distance measure. the direct $L_2$ norm is found adequate

Acoustic-phonetic Features For Refining The Explicit Speech Segmentation

This paper describes the refinement of the automatic speech segmentation into phones obtained via Hidden Markov Models (HMM). This refinement is based on acoustic-phonetic features associated to different phone classes. The proposed system was evaluated using both a small speaker dependent Brazilian Portuguese speech database and a speaker independent speech database (TIMIT). The refinement was applied to the boundaries obtained by just running the Viterbi's algorithm on the HMMs associated to the different utterances. Improvements of 30% and 13% were achieved in the percentage of segmentation errors below 20 ms for the speaker dependent and speaker independent databases respectively.318531856Vidal, E., Marzal, A., A review and new approaches for automatic segmentation of speech signals (1990) Signal Processing V: Theories and Applications, pp. 43-53. , L. Torres, E. Masgrau and M. A. Lagunas eds, Elsevier Science Publishers B. V, ppToledano, D., Gómez, L.A., Grande, L.V., Automatic Phonetic Segmentation (2003) IEEE Transactions on Speech and Audio Processing, 11 (6). , Novembervan Hemert, J.P., Automatic Segmentation of Speech (1991) IEEE Transactions on Signal Processing, 39 (4), pp. 1008-1012. , AprilSaiJayram, A.K.V., Ramasubramanian, V., Sreenivas, T.V., Robust parameters for automatic segmentation of speech (2002) Proceedings of Acoustics, Speech and Signals Processing, 1, pp. 513-512Hatazaki, K., Komor, Y., Kawabata, T., Shikano, K., Phoneme segmentation using spectrogram reading knowledge (1989) Proceeding of the International Conference on Acoustics Speech and Signal Processing, pp. 393-396Selmini, A. M. and Violaro, F. Improving the Explicit Automatic Speech Segmentation Provided by HMMs, Proceedings of the International Workshop on Telecommunications, pp. 220-226, Santa Rita do Sapucaí, Brazil, 2007Wang, L., Zhao, Y., Chu, M., Zhou, J., Cao, Z., Refining segmental boundaries for TTS database using fine contextual-dependent boundary models (2004) Proceedings of the ICASP, 1, pp. 641-644. , Beijing, China, MayDemuynck, K., Laureys, T., A comparison of different approaches to automatic speech segmentation (2002) Proceedings of the 5th International Conference on Text, Speech and Dialogue, , Brno, Czech Republic, SeptemberJuneja, A., Speech Recognition Based on Phonetic Features and Acoustic Landmarks (2004), PhD Thesis, University of Maryland, College Park, USAHosom, J.P., Automatic Phoneme Alignment Based on Acoustic-Phonetic Modeling (2002) 7th International Conference on Spoken Language Processing, , Denver, CO, USA, Septembe