Modern phonetic research regularly makes use of automatic tools for the annotation of speech data, however few tools exist for the annotation of many variable phonetic phenomena. At the same time, pre-trained self-supervised models, such as wav2vec2.0, have been shown to perform well at speech classification tasks and latently encode fine-grained phonetic information. We demonstrate that wav2vec2.0 models can be trained to automatically classify stop burst presence with high accuracy in both English and Japanese, robust across both finely-curated and unprepared speech corpora. Patterns of variability in stop realisation are replicated with the automatic annotations, and closely follow those of manual annotations. These results demonstrate the potential of pre-trained speech models as tools for the automatic annotation and processing of speech corpus data, enabling researchers to 'scale-up' the scope of phonetic research with relative ease.

Speech rate has been shown to vary across social categories such as gender, age, and dialect, while also being conditioned by properties of speech planning. The effect of utterance length, where speech rate is faster and less variable for longer utterances, has also been shown to reduce the role of social factors once it has been accounted for, leaving unclear the relationship between social factors and speech production in conditioning speech rate. Through modelling of speech rate across 13 English speech corpora, it is found that utterance length has the largest effect on speech rate, though this effect itself varies little across corpora and speakers. While age and gender also modulate speech rate, their effects are much smaller in magnitude. These findings suggest utterance length effects may be conditioned by articulatory and perceptual constraints, and that social influences on speech rate should be interpreted in the broader context of how speech rate variation is structured.

While much work has considered the problem of latent attribute inference for users of social media such as Twitter, little has been done on non-English-based content and users.Here, we conduct the first assessment of latent attribute inference in languages beyond English, focusing on gender inference.We find that the gender inference problem in quite diverse languages can be addressed using existing machinery.Further, accuracy gains can be made by taking language-specific features into account.We identify languages with complex orthography, such as Japanese, as difficult for existing methods, suggesting a valuable direction for future research.

Previous work on automatic VOT measurement has focused on positive-valued VOT. However, in many languages VOT can be either positive or negative (“prevoiced”). We present a discriminative algorithm that simultaneously decides whether a stop is prevoiced and measures its VOT. The algorithm operates on feature functions designed to locate the burst and voicing onsets in the positive and negative VOT cases. Tested on a database of positive- and negative-VOT voiced stops, the algorithm predicts prevoicing with>90 % accuracy, and gives good agreement between automatic and manual measurements. Index Terms: voice onset time, automatic phonetic measurement, discriminative methods, structured prediction

Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., and Singer, Y. (2006). Online passive-aggressivealgorithms. The Journal of Machine Learning Research, 7:551–585.Das, S. and Hansen, J. (2004). Detection of Voice Onset Time (VOT) for unvoiced stops (/p/,/t/,/k/) using the TeagerEnergy Operator (TEO) for automatic detection of accented English. In Proc. 6th NORSIG, pp. 344–347.Fischer, E. and Goberman, A. (2010). Voice onset time in Parkinson disease. J. Comm. Disorders, 43:21–34.Kazemzadeh, A., Tepperman, J., Silva, J., You, H., Lee, S., Alwan, A., and Narayanan, S. (2006). Automaticdetection of voice onset time contrasts for use in pronunciation assessment. In Proc. INTERSPEECH,pp. 721–724.Keshet, J., Shalev-Shwartz, S., Singer, Y., and Chazan, D. (2005). Phoneme alignment based on discriminativelearning. In Proc. INTERSPEECH, pp. 2961–2964.Keshet, J., Shalev-Shwartz, S., Singer, Y., and Chazan, D. (2007). A large margin algorithm for speech-to-phonemeand music-to-score alignment. IEEE Trans. Audio, Speech, Language Process., 15(8):2373–2382.Kuhl, P. and Miller, J. (1978). Speech perception by the chinchilla: Identification functions for synthetic VOT stimuli.J. Acoust. Soc. America, 63(3):905–917.Morris, R., McCrea, C., and Herring, K. (2008b). Voice onset time differences between adult males and females:Isolated syllables. J. Phonetics, 36(2):308–317.Stouten, V. and van Hamme, H. (2009). Automatic voice onset time estimation from reassignment spectra. SpeechCommunication, 51(12):1194–1205.Taskar, B., Guestrin, C., and Koller, D. (2004). Max-margin Markov networks. Advances in Neural InformationProcessing Systems, 16.Theodore, R., Miller, J., and DeSteno, D. (2009b). Individual talker differences in voice-onset-time: Contextualinfluences. J. Acoust. Soc. America, 125:3974–3982.Tsochantaridis, I., Hofmann, T., Joachims, T., and Altun, Y. (2004). Support vector machine learning forinterdependent and structured output spaces. In Proc. 21st ICML.Yao, Y. (2007). Closure duration and VOT of word-initial voiceless plosives in English in spontaneous connectedspeech. UC Berkeley Phonology Lab Annual Report, pp. 183–225.