by compensating the speech signal using specific warping factor. Experiments are conducted using TIMIT speech corpus and Hidden Markov Model Toolkit (HTK) together with the implementation of VTLN method in order to show improvement in speaker independent phoneme recognition. The results show better recognition

In anticipation of upcoming mobile telephony services with higher speech quality, a wideband (50 Hz to 7 kHz) mobile telephony deriva-tive of TIMIT has been recorded called WTIMIT. It opens up various scientific investigations; e.g., on speech quality and intelligibility, as well as on wideband

-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. These materials are available to members of the Linguistic Data Consortium; for details, see Section 5.1.

This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "

In this paper we present preliminary results of speaker normalization procedures that were tested with all 35,385 stressed vowels of 438 male speakers in the TIMIT speech corpus. First we investigate a procedure to reduce the variance in vowel space. This procedure knows about the identity

In the case of a trlgr~m language model, the proba-bility of the next word conditioned on the previous two words is estimated from a large corpus of text. The re-sulting static trigram language model (STLM) has fixed probabilities that are independent of the document being dictated. To improve

In anticipation of upcoming mobile telephony services with higher speech quality, a wideband (50 Hz to 7 kHz) mobile telephony deriva-tive of TIMIT has been recorded called WTIMIT. It opens up various scientific investigations; e.g., on speech quality and intelligibility, as well as on wideband

A comparative evaluation of speech enhancement algorithms for robust automatic speech recognition is presented. The evaluation is performed on a core test set of the TIMIT speech corpus. Mean objective speech quality scores as well as ASR correctness scores under two noise conditions are given

The QUT-NOISE-TIMIT corpus consists of 600 hours of noisy speech sequences designed to enable a thorough evaluation of voice activity detection (VAD) algorithms across a wide variety of common background noise scenarios. In order to construct the final mixed-speech database, a collection of over 10

This paper describes a new speech corpus, STC-TIMIT, and discusses the process of design, development and its distribution through LDC. The STC-TIMIT corpus is derived from the widely used TIMIT corpus by sending it through a real and single telephone channel. TIMIT is phonetically balanced, covers