Learning phonetic features from waveforms
نویسنده
چکیده
Unsupervised learning of broad phonetic classes by infants was simulated using a statistical mixture model. With the phonetic labels removed, hand-transcribed segments from the TIMIT database were used in model-based clustering to obtain data-driven classes. Simple Hidden Markov Models were chosen to be the components of the mixture, with Mel-Cepstral coefficients as the front-end. The sound classes were found by iteratively partitioning the clusters. The results of running this algorithm on the TIMIT segments suggest that the partitions may be interpreted as gradient acoustic features, and that to some degree, the resulting clusters correspond to knowledge-based phonetic classes. Thus, the clusters may reflect the preliminary phonological categories formed during language learning in early childhood.
منابع مشابه
Weak representational bias and the discovery of linguistic categories from speech waveforms
How does a child start to learn the sound patterns of her native language? There are two diametrically opposed approaches to this problem. According to one approach, the learner always looks for a symbolic representation in input, and such representation is based on a set of universal phonetic features (Chomsky and Halle, 1968). Since phonetic segments – or more intuitively “speech sounds” – ar...
متن کاملSpeaker Identification Using Glottal-Source Waveforms and Support-Vector-Machine Modelling
Speaker identification experiments are performed with novel features representative of the glottal source waveform. These are derived from closed-phase analysis and inverse filtering. Source waveforms are segmented into two consecutive periods and normalised in prosody, forming so called source-frame feature vectors. Support-vector-machines are used to construct speaker discriminative hyperplan...
متن کاملClassification of Fricatives Using Feature Extrapolation of Acoustic-Phonetic Features in Telephone Speech
This paper proposes a classification module for fricative consonants in telephone speech using an acoustic-phonetic feature extrapolation technique. In channel-deteriorated telephone speech, acoustic cues of fricative consonants are expected to be degraded or missing due to limited bandwidth. This paper applies an extrapolation technique to acoustic-phonetic features based on Gaussian mixture m...
متن کاملA model of generalization in distributional learning of phonetic categories
Computational work in the past decade has produced several models accounting for phonetic category learning from distributional and lexical cues. However, there have been no computational proposals for how people might use another powerful learning mechanism: generalization from learned to analogous distinctions (e.g., from /b/–/p/ to /g/–/k/). Here, we present a new simple model of generalizat...
متن کاملPhonetic segmentation using multiple speech features
In this paper we propose a method for improving the performance of the segmentation of speech waveforms to phonetic segments. The proposed method is based on the well known Viterbi timealignment algorithm and utilizes the phonetic boundary predictions from multiple speech parameterization techniques. Specifically, we utilize the best, with respect to boundary type, phone transition position pre...
متن کامل