Hybrid Autoregressive Resonance Estimation and Density Mixture Formant Tracking Model

Autor(es)

Ramirez, Miguel Arjona

• 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
• Modalidade de apoio: Auxílio à Pesquisa - Regular