Whispered Speech to Normal Speech Conversion Using Bidirectional LSTMs with Meta-network

Yu, Wei; Lian, Hailun; Zhou, Jian; Wang, Huabin; Tao, Liang

doi:10.1109/icicsp48821.2019.8958537

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…In Yu et al (2019), a RNN (Recurrent Neural Network) based Bi-directional LSTM (BLSTM) approach is used for the remapping tasks. BLSTM based converters are known to produce high quality conversions in terms of naturalness but tend to suffer from model complexity and inference cost (Nisha Meenakshi and Ghosh 2018).…”

Section: Meta-blstmmentioning

confidence: 99%

“…Some version of a dataset derived from the TIMIT corpus was used in all cases included here. Specifically, whispered TIMIT (wTimit) 1 was used by Niranjan et al (2020), Patel et al (2021), Parmar et al (2019), andPatel et al (2019), while CSTR-NAM-TIMIT Plus was used by Gao et al (2021), Lian et al (2019a), Malaviya et al (2020), Pang et al (2020), Yu et al (2019), and Lian et al (2019b). The wTIMIT dataset uses the prompts in TIMIT, a well-known corpus often used for benchmarking in speech recognition, including 450 phonetically balanced sentences both in normal and whispered speech.…”

Section: Datasetsmentioning

confidence: 99%

“…A higher MCD value indicates a greater difference between the converted and the reference speech (lower is better). Additionally, Short-Time Objective Intelligibility (STOI) was also used in Lian et al (2019a), Pang et al (2020), Yu et al (2019), andLian et al (2019b) and Perceptual Evaluation of Speech Quality was also used in Lian et al (2019a), Pang et al (2020), andYu et al (2019). Finally, the two remaining papers used entirely different metrics from the rest.…”

Section: Objective Metricsmentioning

confidence: 99%

“…Additionally, in Patel et al (2021), the Kullback-Leibler Divergence (KLD) and Jensen-Shannon Divergence (JSD) between predicted and original F0 for speaker-specific tasks were also considered. In Niranjan et al (2020), F0 is not estimated and in Pang et al (2020), Yu et al (2019), andLian et al (2019b), while the F0 estimation is discussed and accompanied with illustrations of the F0 curve, no objective metric is presented.…”

Section: Objective Metricsmentioning

confidence: 99%

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Machine Learning Approaches for Whisper to Normal Speech Conversion

Oliveira

2022

UPjeng

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Whispered speech is a mode of speech that differs from normal speech due to the absence of a periodic component, namely the Fundamental Frequency that characterizes the pitch, among other spectral and temporal differences. Much attention has been given in recent years to the application of Machine Learning techniques for voice conversion tasks. The whisper-to-normal speech conversion is particularly challenging, however, especially with respect to the Fundamental Frequency estimation. Based on the most recent literature, this survey assesses the state-of-the-art regarding Machine Learning based whisper-to-normal speech conversion, identifying trends both on modeling and training approaches. The proposed solutions include Generative Adversarial Network based, Autoencoder based and Bidirectional Long Short-Term Memory based frameworks, among other Deep Neural Network based architectures. In addition to Parallel versus Non-Parallel training, time-alignment requirements and strategies, datasets, vocoder usage, as well as both objective and subjective evaluation metrics are also covered by the present survey.

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Section: Meta-blstmmentioning

confidence: 99%