Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech. They combine the best features of classic statistical speech synthesis and modern neural TTS, requiring less data and fewer training updates, and are less prone to gibberish output caused by neural attention failures. In this paper, we combine neural HMM TTS with normalising flows for describing the highly non-Gaussian distribution of speech acoustics. The result is a powerful, fully probabilistic model of durations and acoustics that can be trained using exact maximum likelihood. Experiments show that a system based on our proposal needs fewer updates than comparable methods to produce accurate pronunciations and a subjective speech quality close to natural speech.

Disfluencies are a hallmark of spontaneous speech and play an important role in conversation, yet have been shown to negatively impact judgments about speakers. We explored the role of disfluencies in the perception of competence, sincerity and confidence in public speaking contexts, using synthesized spontaneous speech. In one experiment, listeners rated 30-40-second clips which varied in terms of whether they contained filled pauses, as well as the number and types of repetition. Both the overall number of disfluencies and the repetition type had an impact on competence and confidence, and disfluent speech was also rated as less sincere. In the second experiment, the negative effects of repetition type on competence were attenuated when participants attributed disfluency to anxiety.

As part of the PSST challenge, we explore how data augmentations, data sources, and model size affect phoneme transcription accuracy on speech produced by individuals with aphasia. We evaluate model performance in terms of feature error rate (FER) and phoneme error rate (PER). We find that data augmentations techniques, such as pitch shift, improve model performance. Additionally, increasing the size of the model decreases FER and PER. Our experiments also show that adding manually-transcribed speech from non-aphasic speakers (TIMIT) improves performance when Room Impulse Response is used to augment the data. The best performing model combines aphasic and non-aphasic data and has a 21.0% PER and a 9.2% FER, a relative improvement of 9.8% compared to the baseline model on the primary outcome measurement. We show that data augmentation, larger model size, and additional non-aphasic data sources can be helpful in improving automatic phoneme recognition models for people with aphasia.

Spontaneous speech synthesis is a complex enterprise, as the data has large variation, as well as speech disfluencies nor-mally omitted from read speech. These disfluencies perturb the attention mechanism present in most Text to Speech (TTS) sys-tems. Explicit modelling of prosodic features has enabled intu-itive prosody modification of synthesized speech. Most pros-ody-controlled TTS, however, has been trained on read-speech data that is not representative of spontaneous conversational prosody. The diversity in prosody in spontaneous speech data allows for more wide-ranging data-driven modelling of pro-sodic features. Additionally, prosody-controlled TTS requires extensive training data and GPU time which limits accessibil-ity. We use neural HMM TTS as it reduces the parameter size and can achieve fast convergence with stable alignments for spontaneous speech data. We modify neural HMM TTS to ena-ble prosodic control of the speech rate and fundamental fre-quency. We perform subjective evaluation of the generated speech of English and Swedish TTS models and objective eval-uation for English TTS. Subjective evaluation showed a signif-icant improvement in naturalness for Swedish for the mean prosody compared to a baseline with no prosody modification, and the objective evaluation showed greater variety in the mean of the per-utterance prosodic features.

Although the paralinguistic and phonological significance of breathy voice is well known, its pragmatic roles have been little studied. We report a systematic exploration of the pragmatic functions of breathy voice in American English, using a small corpus of casual conversations, using the Cepstral Peak Prominence Smoothed measure as an indicator of breathy voice, and using a common workflow to find prosodic constructions and identify their meanings. We found two prosodic constructions involving breathy voice. The first involves a short region of breathy voice in the midst of a region of low pitch, functioning to mark self-directed speech. The second involves breathy voice over several seconds, combined with a moment of wider pitch range leading to a high pitch over about a second, functioning to mark an attempt to establish common ground. These interpretations were confirmed by a perception experiment.

Much of the research investigating the perception of speaker certainty has relied on either attempting to elicit prosodic features in read speech, or artificial manipulation of recorded audio. Our novel method of controlling prosody in synthesized spontaneous speech provides a powerful tool for studying speech perception and can provide better insight into the interacting effects of prosodic features on perception while also paving the way for conversational systems which are more effectively able to engage in and respond to social behaviors. Here we have used this method to examine the combined impact of filled pause location, speech rate and f0 on the perception of speaker confidence. We found an additive effect of all three features. The most confident-sounding utterances had no filler, low f0 and high speech rate, while the least confident-sounding utterances had a medial filled pause, high f0 and low speech rate. Insertion of filled pauses had the strongest influence, but pitch and speaking rate could be used to more finely control the uncertainty cues in spontaneous speech synthesis.

Smiling during speech production has been shown to result in perceptible acoustic differences compared to non-smiling speech. However, there is a scarcity of research on the perception of “smiling voice” in synthesized spontaneous speech. In this study, we used a sequence-to-sequence neural text-tospeech system built on conversational data to produce utterances with the characteristics of spontaneous speech. Segments of speech following laughter, and the same utterances not preceded by laughter, were compared in a perceptual experiment after removing laughter and/or breaths from the beginning of the utterance to determine whether participants perceive the utterances preceded by laughter as sounding as if they were produced while smiling. The results showed that participants identified the post-laughter speech as smiling at a rate significantly greater than chance. Furthermore, the effect of content (positive/neutral/negative) was investigated. These results show that laughter, a spontaneous, non-elicited phenomenon in our model’s training data, can be used to synthesize expressive speech with the perceptual characteristics of smiling.