Phone-aware LSTM-RNN for voice conversion

Lai, Jiahao; Chen, Bin; Tan, Tian; Tong, Sibo; Yu, Kai

doi:10.1109/icsp.2016.7877819

Cited by 14 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mel-cepstral distortion (MCD) [246] is commonly used to measure the difference between two spectral features [62], [67], [256], [257]. It is calculated between the converted and target Mel-cepstral coefficients, or MCEPs, [258], [259], y and y.…”

Section: A Objective Evaluation 1) Spectrum Conversionmentioning

confidence: 99%

An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

Şişman

Yamagishi

King

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

208

View full text Add to dashboard Cite

Section: A Objective Evaluation 1) Spectrum Conversionmentioning

confidence: 99%

An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

Şişman

Yamagishi

King

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

208

View full text Add to dashboard Cite

“…The alignment is usually applied directly by Dynamic Time Wrapping (DTW) [17]. Also, there are techniques to get a more accurate feature alignment with the help of automatic speech recognition (ASR) techniques [18,14,19]. The aligned feature sequences x = x1, .., xT and y = y1, .., yT are then converted frame by frame in different methods (e.g.…”

Section: Parallel Data Voice Conversionmentioning

confidence: 99%

“…The Mel-spectrograms are extracted through a short-time Fourier transform (STFT) using a 50ms frame size, 12.5 ms frame hop and a Hann window function as in [10]. The baseline system uses the same LSTM-RNN voice conversion system in [14]. The converted acoustic features are vocoded into speech waveform using both MLSA and Mcep-based WaveNet vocoder [8].…”

Section: Experiments Setupmentioning

confidence: 99%

“…The Mceps are extracted at 5ms frame shift. But different from [6], we use the conversion model in [14] and trained a speaker dependent WaveNet Vocoder using 8 bits µ-law.…”

Section: Experiments Setupmentioning

confidence: 99%

“…The converted features are then vocoded into waveform using a Mel-spectrogram based WaveNet vocoder. A Mcep-based voice conversion system we proposed before [14] (Group 'G' in VCC2016 [15]) is used for comparison. The Melspectrogram and Mcep in different systems are trained using similar LSTM-RNN neural networks for frame-to-frame feature mapping.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-quality Voice Conversion Using Spectrogram-Based WaveNet Vocoder

Chen¹,

Chen

Lai

et al. 2018

Interspeech 2018

Self Cite

View full text Add to dashboard Cite

Waveform generator is a key component in voice conversion. Recently, WaveNet waveform generator conditioned on the Mel-cepstrum (Mcep) has shown better quality over standard vocoder. In this paper, an enhanced WaveNet model based on spectrogram is proposed to further improve voice conversion performance. Here, Mel-frequency spectrogram is converted from source speaker to target speaker using an LSTM-RNN based frame-to-frame feature mapping. To evaluate the performance, the proposed approach is compared to an Mcep based LSTM-RNN voice conversion system. Both STRAIGHT vocoder and Mcep-based WaveNet vocoder are elected to produce the converted speech for Mcep conversion system. The fundamental frequency (F0) of the converted speech in different systems is analyzed. The naturalness, similarity and intelligibility are evaluated in subjective measures. Results show that the spectrogram based WaveNet waveform generator can achieve better voice conversion quality compared to traditional WaveNet approaches. The Mel-spectrogram based voice conversion can achieve significant improvement in speaker similarity and inherent F0 conversion.

show abstract