Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech

Nakamura, Keigo; Toda, Tomoki; Saruwatari, Hiroshi; Shikano, Kiyohiro

doi:10.1016/j.specom.2011.07.007

Cited by 158 publications

(106 citation statements)

References 17 publications

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“…된 근전도 신호로부터 음성 신호를 추정하는 방법, [2] 작은 목소리로 속삭일 때 얼굴의 근육부위에서 음 성의 미세한 진동을 취득하는 NAM(Non-Audible Microphone) [3] 을 사용하는 방법, GHz microwave를 입 주변 에 쏘고, 되돌아오는 신호의 도플러를 이용한 방법, [4] 초음파 도플러를 이용한 방법 [5] 등이 있다. …”

Section: 초음파 도플러 신호를 이용한 음성 합성unclassified

Speech synthesis using acoustic Doppler signal

Lee¹

2016

The Journal of the Acoustical Society of Korea

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In this paper, a method synthesizing speech signal using the 40 kHz ultrasonic signals reflected from the articulatory muscles was introduced and performance was evaluated. When the ultrasound signals are radiated to articulating face, the Doppler effects caused by movements of lips, jaw, and chin observed. The signals that have different frequencies from that of the transmitted signals are found in the received signals. These ADS (Acoustic-Doppler Signals) were used for estimating of the speech parameters in this study. Prior to synthesizing speech signal, a quantitative correlation analysis between ADS and speech signals was carried out on each frequency bin. According to the results, the feasibility of the ADS-based speech synthesis was validated. ADS-to-speech transformation was achieved by the joint Gaussian mixture model-based conversion rules. The experimental results from the 5 subjects showed that filter bank energy and LPC (Linear Predictive Coefficient) cepstrum coefficients are the optimal features for ADS, and speech, respectively. In the subjective evaluation where synthesized speech signals were obtained using the excitation sources extracted from original speech signals, it was confirmed that the ADS-to-speech conversion method yielded 72.2 % average recognition rates.

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Section: 초음파 도플러 신호를 이용한 음성 합성unclassified

Speech synthesis using acoustic Doppler signal

Lee¹

2016

The Journal of the Acoustical Society of Korea

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“…To improve naturalness of EL speech, we have proposed several EL speech enhanced methods based on statistical voice conversion techniques [7]- [9]. In these methods, acoustic features of EL speech are converted into those of normal speech using Gaussian mixture models (GMMs) [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…In these methods, acoustic features of EL speech are converted into those of normal speech using Gaussian mixture models (GMMs) [7]- [9]. We have shown that F 0 pattern replacement from the mechanically generated ones into those predicted from the spectral sequence of the EL speech using the GMM significantly improves naturalness of EL speech while preserving its intelligibility [9].…”

Section: Introductionmentioning

confidence: 99%

A Vibration Control Method of an Electrolarynx Based on Statistical <i>F</i><sub>0</sub> Pattern Prediction

Tanaka

Toda

Nakamura

2017

IEICE Trans. Inf. & Syst.

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SUMMARY This paper presents a novel speaking aid system to help laryngectomees produce more naturally sounding electrolaryngeal (EL) speech. An electrolarynx is an external device to generate excitation signals, instead of vibration of the vocal folds. Although the conventional EL speech is quite intelligible, its naturalness suffers from the unnatural fundamental frequency (F 0 ) patterns of the mechanically generated excitation signals. To improve the naturalness of EL speech, we have proposed EL speech enhancement methods using statistical F 0 pattern prediction. In these methods, the original EL speech recorded by a microphone is presented from a loudspeaker after performing the speech enhancement. These methods are effective for some situation, such as telecommunication, but it is not suitable for face-to-face conversation because not only the enhanced EL speech but also the original EL speech is presented to listeners. In this paper, to develop an EL speech enhancement also effective for face-to-face conversation, we propose a method for directly controlling F 0 patterns of the excitation signals to be generated from the electrolarynx using the statistical F 0 prediction. To get an "actual feel" of the proposed system, we also implement a prototype system. By using the prototype system, we find latency issues caused by a real-time processing. To address these latency issues, we furthermore propose segmental continuous F 0 pattern modeling and forthcoming F 0 pattern modeling. With evaluations through simulation, we demonstrate that our proposed system is capable of effectively addressing the issues of latency and those of electrolarynx in term of the naturalness.

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“…In recent years, speech synthesis techniques have been used for several applications such as speaking-aid systems [8], [9] and speech translation systems [10], [11]. These applications often require such synthesis systems that can generate users' voices.…”

Section: Introductionmentioning

confidence: 99%

Statistical Bandwidth Extension for Speech Synthesis Based on Gaussian Mixture Model with Sub-Band Basis Spectrum Model

Ohtani

Tamura

Morita

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper describes a novel statistical bandwidth extension (BWE) technique based on a Gaussian mixture model (GMM) and a sub-band basis spectrum model (SBM), in which each dimensional component represents a specific acoustic space in the frequency domain. The proposed method can achieve the BWE from speech data with an arbitrary frequency bandwidth whereas the conventional methods perform the conversion from fixed narrow-band data. In the proposed method, we train a GMM with SBM parameters extracted from full-band spectra in advance. According to the bandwidth of input signal, the trained GMM is reconstructed to the GMM of the joint probability density between low-band SBM and high-band SBM components. Then high-band SBM components are estimated from low-band SBM components of the input signal based on the reconstructed GMM. Finally, BWE is achieved by adding the spectra decoded from estimated high-band SBM components to the ones of the input signal. To construct the full-band signal from the narrow-band one, we apply this method to log-amplitude spectra and aperiodic components. Objective and subjective evaluation results show that the proposed method extends the bandwidth of speech data robustly for the log-amplitude spectra. Experimental results also indicate that the aperiodic component extracted from the upsampled narrow-band signal realizes the same performance as the restored and the full-band aperiodic components in the proposed method. key words: speech enhancement, voice conversion, bandwidth extension, sub-band basis spectrum model, Gaussian mixture model

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Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech

Cited by 158 publications

References 17 publications

Speech synthesis using acoustic Doppler signal

Speech synthesis using acoustic Doppler signal

A Vibration Control Method of an Electrolarynx Based on Statistical <i>F</i><sub>0</sub> Pattern Prediction

Statistical Bandwidth Extension for Speech Synthesis Based on Gaussian Mixture Model with Sub-Band Basis Spectrum Model

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