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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Hybrid Autoregressive Resonance Estimation and Density Mixture Formant Tracking Model

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Autor(es):
Ramirez, Miguel Arjona
Número total de Autores: 1
Tipo de documento: Artigo Científico
Fonte: IEEE ACCESS; v. 6, p. 30217-30224, 2018.
Citações Web of Science: 0
Resumo

A novel formant tracker is proposed using the mixture models of t densities (tMMs) for vocal tract resonance frequencies estimated with a hybrid linear prediction (HLP) method. The hybrid integer-cycle pitch-synchronous linear prediction (LP) analysis improves the frequency resolution over voiced segments, leading to closer formant estimates than those provided by other LP methods. In conjunction with HLP, formant trajectories are shown to be more nearly tracked by tMMs than by Gaussian density models. Tests with synthetic voiced and whispered speech as well as with an annotated database confirm better performance than either tMM clustering after formant estimation based on different time-frequency representations or tracking after different LP methods. (AU)

Processo FAPESP: 15/25512-0 - Análise Condicional para Codificação e Reconhecimento de Sinais de Áudio e Voz
Beneficiário:Miguel Arjona Ramírez
Linha de fomento: Auxílio à Pesquisa - Regular