Publication

Preliminary Work on Speaker Adaptation for DNN-Based Speech Synthesis

2015
Reports

Abstract

We investigate speaker adaptation in the context of deep neural network (DNN) based speech synthesis. More specifically, our current work focuses on the exploitation of auxiliary information such as gender, speaker identity or age during the DNN training process. The proposed technique is compared to standard acoustic feature transformations such as the feature based maximum likelihood linear regression (FMLLR) based speaker adaptation. Objective error measurements as well as perceptual experiments, performed on the WSJCAM0 database, suggest that the proposed method is superior to standard feature transformations.

Official source

https://infoscience.epfl.ch/entities/publication/f900ade0-e1be-4f73-9f74-2831ab4a74f7

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2015
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/f900ade0-e1be-4f73-9f74-2831ab4a74f7

About this result

Related concepts (15)

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Speaker recognition

Speaker recognition is the identification of a person from characteristics of voices. It is used to answer the question "Who is speaking?" The term voice recognition can refer to speaker recognition or speech recognition. Speaker verification (also called speaker authentication) contrasts with identification, and speaker recognition differs from speaker diarisation (recognizing when the same speaker is speaking).

Speech synthesis

Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database.

Related publications (16)

Preliminary Work on Speaker Adaptation for DNN-Based Speech Synthesis

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