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Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed “speech-inversion.” This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

The ability to automatically detect stuttering events in speech could help speech pathologists track an individual's fluency over time or help improve speech recognition systems for people with atypical speech patterns. Despite increasing interest in this area, existing public datasets are too small to build generalizable dysfluency detection systems and lack sufficient annotations. In this work, we introduce Stuttering Events in Podcasts (SEP-28k), a dataset containing over 28k clips labeled with five event types including blocks, prolongations, sound repetitions, word repetitions, and interjections. Audio comes from public podcasts largely consisting of people who stutter interviewing other people who stutter. We benchmark a set of acoustic models on SEP-28k and the public FluencyBank dataset and highlight how simply increasing the amount of training data improves relative detection performance by 28% and 24% F1 on each. Annotations from over 32k clips across both datasets will be publicly released.

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Background noise and channel degradations seriously constrain the performance of state-of-the-art speech recognition systems. Studies comparing human speech recognition performance with automatic speech recognition systems indicate that the human auditory system is highly robust against background noise and channel variabilities compared to automated systems. A traditional way to add robustness to a speech recognition system is to construct a robust feature set for the speech recognition model. In this work, we present an amplitude modulation feature derived from Teager's nonlinear energy operator that is power normalized and cosine transformed to produce normalized modulation cepstral coefficient (NMCC) features. The proposed NMCC features are compared with respect to state-of-the-art noise-robust features in Aurora-2 and a renoised Wall Street Journal (WSJ) corpus. The WSJ word-recognition experiments were performed on both a clean and artificially renoised WSJ corpus using SRI's DECIPHER large vocabulary speech recognition system. The experiments were performed under three train-test conditions: (a) matched, (b) mismatched, and (c) multi-conditioned. The Aurora-2 digit recognition task was performed using the standard HTK recognizer distributed with Aurora-2. Our results indicate that the proposed NMCC features demonstrated noise robustness in almost all the training-test conditions of renoised WSJ data and also improved digit recognition accuracies for Aurora-2 compared to the MFCCs and state-of-the-art noise-robust features

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