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The electrolarynx (EL) is an electromechanical device that enables patients to produce voice following the surgical removal of their larynx. The purpose of this study is to understand the acoustic and perceptual characteristics of Mandarin consonants produced by EL speakers. First, the acoustic characteristics (including speech intensity, consonant duration, spectral peak, and F2 onset) of Mandarin EL consonants are investigated by comparing the EL and normal consonants. Then, a perceptual evaluation of EL consonants is conducted to identify the relationship between acoustical characteristics and perceptual intelligibility. The results suggest three consonant confusion types are mainly responsible for the poor intelligibility of Mandarin EL consonants: (1) the “unaspirated-for-aspirated” confusion caused by the significantly shortened voice onset time of aspirated consonants; (2) the “voiced-for-voiceless” confusion caused by the continuous pulsing of the EL device and low consonant intensity; and (3) the “perceptual omission” caused by low intensity of consonants and / or consonant omission. The results obtained are promising and potential for further improvements in Mandarin EL speech intelligibility.

We created an electrolarynx (EL) that generates tonal distinctions in Mandarin.Tones are identified with greater accuracy in the tonal vs. monotone EL condition.Tonal information enhances EL speech intelligibility and acceptability. An electrolarynx (EL) is a valuable rehabilitative option for individuals who have undergone laryngectomy, but current monotone ELs do not support controlled variations in fundamental frequency for producing tonal languages. The present study examined the production and perception of Mandarin Chinese using a customized hand-held EL driven by computer software to generate tonal distinctions (tonal EL). Four native Mandarin speakers were trained to articulate their speech coincidentally with preprogrammed tonal patterns in order to produce mono- and di-syllabic words with a monotone EL and tonal EL. Three native Mandarin speakers later transcribed and rated the speech samples for intelligibility and acceptability. Results indicated that words produced using the tonal EL were significantly more intelligible and acceptable than those produced using the monotone EL.

The majority of laryngectomees use the electrolarynx as their primary mode of verbal communication after total laryngectomy surgery. However, the archetypal electrolarynx suffers from a monotonous tone and the inconvenience of requiring manual control. This paper presents the potential of pattern recognition to support electrolarynx use by predicting fundamental frequency (F0) and voicing state (VS) from surface EMG of the infrahyoid and suprahyoid muscles, as well as from a respiratory trace. In this study, surface EMG signals from the infrahyoid and suprahyoid muscle groups and respiratory trace were collected from 10 able-bodied, adult males (18–60 years old). Participants performed three kinds of vocal tasks – tones, legatos and phrases. Signal features were extracted from the EMG and respiratory trace, and a Support Vector Machine (SVM) classifier with radial basis function kernels was employed to predict F0 and voicing state. An average root mean squared error of 2.81 ± 0.6 semitones was achieved for the estimation of vocal frequency in the range of 90–360 Hz. An average cross-validation (CV) accuracy of 78.05 ± 6.3% was achieved for the prediction of voicing state from EMG and 65.24 ± 7.8% from the respiratory trace. The proposed method has the advantage of being non-invasive compared with studies that relied on intramuscular electrodes (invasive), while still maintaining an accuracy above chance. Pattern classification of neck-muscle surface EMG has merit in the prediction of fundamental frequency and voicing state during vocalization, encouraging further study of automatic pitch modulation for electrolarynges and silent speech interfaces.

The electrolarynx (EL) is a widely used device for alaryngeal communication, but the low quality seriously reduces the intelligibility of EL speech. To improve EL speech quality, a vowel-specific voice source based on visual information of lip shape and movements and artificial neural network (ANN) is implemented into an experimental EL (SGVS-EL) system in real time. Five volunteers (one laryngectomee and four normal speakers) participated in the experimental evaluation of the method and SGVS-EL system. Using ANN participants were able to perform high vowel precision with identification rates of >90% after the training. The results of voicing control indicated that all subjects using SGVS-EL could achieve good vowel control performance in real time, but still control errors frequently occurred at the voice initiation period. However, the control errors had no significantly impact on the perception of SGVS-EL speech. Intelligibility evaluation demonstrated that both the vowels and words produced using the SGVS-EL were more intelligible than vowels spoken with a commercial EL (by 30%) or words (by 18%), respectively. Using a controlled vowel-specific voice source was a feasible and effective way to improve EL speech quality with more intelligible words.

An electrolarynx (EL) is a medical device that generates sound source signals to provide laryngectomees with a voice. In this article we focus on two problems of speech produced with an EL (EL speech). One problem is that EL speech is extremely unnatural and the other is that sound source signals with high energy are generated by an EL, and therefore, the signals often annoy surrounding people. To address these two problems, in this article we propose three speaking-aid systems that enhance three different types of EL speech signals: EL speech, EL speech using an air-pressure sensor (EL-air speech), and silent EL speech. The air-pressure sensor enables a laryngectomee to manipulate the F"0 contours of EL speech using exhaled air that flows from the tracheostoma. Silent EL speech is produced with a new sound source unit that generates signals with extremely low energy. Our speaking-aid systems address the poor quality of EL speech using voice conversion (VC), which transforms acoustic features so that it appears as if the speech is uttered by another person. Our systems estimate spectral parameters, F"0, and aperiodic components independently. The result of experimental evaluations demonstrates that the use of an air-pressure sensor dramatically improves F"0 estimation accuracy. Moreover, it is revealed that the converted speech signals are preferred to source EL speech.