The goal in this paper is to show how the classiication of patterns of phonetic features (=phones) to phonemes can be acquired. This classi-cational process is modelled by a supervised feature selection method, based on a weighted Hamming distance, augmented by Boolean functions describing exceptions. An important aspect is the diierentiation of rules and exceptions during learning. 1 Phonetic ...

This paper describes an Hidden Markov Model-based Punjabi text-to-speech synthesis system (HTS), in which speech waveform is generated from Hidden Markov Models themselves, and applies it to Punjabi speech synthesis using the general speech synthesis architecture of HTK (HMM Tool Kit). This Hidden Markov Model based TTS can be used in mobile phones for stored phone directory or messages. Text m...

It is well known that canonical Spanish, the dialectal variant ‘central’ of Spain, so called Castilian, can be transcribed by rules. This paper deals with the automatic grapheme to phoneme transcription rules in several Spanish dialects from Latin America. Spanish is a language spoken by more than 300 million people, has an important geographical dispersion compared among other languages and ha...

The quantization distortion of vector quantization (VQ) is a key element that affects the performance of a discrete hidden Markov modeling (DHMM) system. Many researchers have realized this problem and tried to use integrated feature or multiple codebook in their systems to offset the disadvantage of the conventional VQ. However the computational complexity of those systems is then increased. I...

In this paper, we present a spectro-temporal feature extraction technique using sub-band Hilbert envelopes of relatively long segments of speech signal. Hilbert envelopes of the sub-bands are estimated using Frequency Domain Linear Prediction (FDLP). Spectral features are derived by integrating the sub-band Hilbert envelopes in short-term frames and the temporal features are formed by convertin...

In this contribution it is shown that a good approach for the grapheme-to-phoneme conversion of proper names (e.g. person names, toponyms, etc), is to use a cascade of a general purpose grapheme-to-phoneme (G2P) converter and a special purpose phoneme-to-phoneme (P2P) converter. The G2P produces an initial transcription that is then transformed by the P2P. The latter is automatically trained on...

This paper describes a method of generating a reduced phoneme set for dialogue-based computer assisted language learning (CALL) systems. We designed a reduced phoneme set consisting of classified phonemes more aligned with the learners’ speech characteristics than the canonical set of a target language. This reduced phoneme set provides an inherently more appropriate model for dealing with misp...

In this work the output density functions of hidden Markov models are phoneme-wise tied mixture Gaussians. For training these tied mixture density HMMs, modiied versions of the Viterbi training and LVQ based corrective tuning are described. The initialization of the mean vectors of the mixture Gaussians is performed by rst composing small Self-Organizing Maps representing each phoneme and then ...

Speaker segmentation is widely used in many tasks such as multi-speaker detection and speaker tracking. The segmentation performance depends on the performance of speaker verification (SV) between two short utterances to a large extent, so the improvement of the SV performance for short utterances would give the segmentation performance a great help. In this paper, a method based on phoneme rec...

Two schemes to obtain phonemic transcriptions of spoken utterances are described and compared. Both schemes utilize the so called Self-Organizing Kohonen Maps first to vector quantize speech into a sequence of phoneme Iabels centisecond apart. In the original scheme, this quasiphoneme sequence is converted into a phoneme string with simple durational transformation rules. In the scheme introduc...