This article reports on the first application of ‘state-of-the-art’ automatic speech recognition (ASR) technology to Xhosa, an indigenous South African language. The project, aimed at developing the technology required to implement a spoken-language front-end for an automatic translation system, is part of a collaborative effort in speech technology research for South African languages. The system we propose to perform this task consists of a speaker independent, isolated word, automatic speech recogniser. During system development speech data from 40 native. Xhosa speakers was recorded, annotated and organised into a database. Two systems were evaluated, one using hidden Markov word models and another implemented with hidden Markov phone models. Recognition rates of up to 95% were achieved.

Numerous attempts have been made to find low-dimensional, formant-related representations of speech signals that are suitable for automatic speech recognition. However, it is often not known how these features behave in comparison with true formants. The purpose of this study was to compare two sets of automatically extracted formant-like features, i.e., robust formants and HMM2 features, to hand-labeled formants. The robust formant features were derived by means of the split Levinson algorithm while the HMM2 features correspond to the frequency segmentation of speech signals obtained by two-dimensional hidden Markov models. Mel-frequency cepstral coefficients (MFCCs) were also included in the investigation as an example of state-of-the-art automatic speech recognition features. The feature sets were compared in terms of their performance on a vowel classification task. The speech data and hand-labeled formants that were used in this study are a subset of the American English vowels database presented in Hillenbrand et al. [J. Acoust. Soc. Am. 97, 3099–3111 (1995)]. Classification performance was measured on the original, clean data and in noisy acoustic conditions. When using clean data, the classification performance of the formant-like features compared very well to the performance of the hand-labeled formants in a gender-dependent experiment, but was inferior to the hand-labeled formants in a gender-independent experiment. The results that were obtained in noisy acoustic conditions indicated that the formant-like features used in this study are not inherently noise robust. For clean and noisy data as well as for the gender-dependent and gender-independent experiments the MFCCs achieved the same or superior results as the formant features, but at the price of a much higher feature dimensionality.

The South African Journal of Communication Disorders (SAJCD) publishes reports and papers concerned with research, and critically evaluative theoretical, philosophical and conceptual issues dealing with aspects of human communication and its disorders, dysphagia, service provision, training and policy.