Goodness of pronunciation (GoP) is typically formulated with Gaussian mixture model-hidden Markov model (GMM-HMM) based acoustic models considering HMM state transition probabilities (STPs) and GMM likelihoods of context dependent phonemes. On the other hand, deep neural network (DNN)-HMM based acoustic models employed sub-phonemic (senone) posteriors instead of GMM likelihoods along with STPs. However, each senone is shared across many states; thus, there is no one-to-one correspondence between them. In order to circumvent this, most of the existing works have proposed modifications to the GoP formulation considering only posteriors neglecting the STPs. In this work, we derive a formulation for the GoP and it results in the formulation involving both senone posteriors and STPs. Further, we illustrate the steps to implement the proposed GoP formulation in Kaldi, a state-of-the-art automatic speech recognition toolkit. Experiments are conducted on English data collected from Indian speakers using acoustic models trained with native English data from LibriSpeech and Fisher-English corpora. The highest improvement in the correlation coefficient between the scores from the formulations and the expert ratings is found to be 14.89% (relative) better with the proposed approach compared to the best of the existing formulations that don't include STPs.

Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem -mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F -scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively.

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