SLT ... : ... IEEE Workshop on Spoken Language Technology : proceedings. IEEE Workshop on Spoken Language Technology最新文献

One-shot voice conversion (VC) aims to convert speech from any source speaker to an arbitrary target speaker with only a few seconds of reference speech from the target speaker. This relies heavily on disentangling the speaker's identity and speech content, a task that still remains challenging. Here, we propose a novel approach to learning disentangled speech representation by transfer learning from style-based text-to-speech (TTS) models. With cycle consistent and adversarial training, the style-based TTS models can perform transcription-guided one-shot VC with high fidelity and similarity. By learning an additional mel-spectrogram encoder through a teacher-student knowledge transfer and novel data augmentation scheme, our approach results in disentangled speech representation without needing the input text. The subjective evaluation shows that our approach can significantly outperform the previous state-of-the-art one-shot voice conversion models in both naturalness and similarity.

Deficits in semantic and pragmatic expression are among the hallmark linguistic features of autism. Recent work in deriving computational correlates of clinical spoken language measures has demonstrated the utility of automated linguistic analysis for characterizing the language of children with autism. Most of this research, however, has focused either on young children still acquiring language or on small populations covering a wide age range. In this paper, we extract numerous linguistic features from narratives produced by two groups of children with and without autism from two narrow age ranges. We find that although many differences between diagnostic groups remain constant with age, certain pragmatic measures, particularly the ability to remain on topic and avoid digressions, seem to improve. These results confirm findings reported in the psychology literature while underscoring the need for careful consideration of the age range of the population under investigation when performing clinically oriented computational analysis of spoken language.

We investigate methods for detecting voiced segments in everyday conversations from ambient recordings. Such recordings contain high diversity of background noise, making it difficult or infeasible to collect representative labelled samples for estimating noise-specific HMM models. The popular utility get-f0 and its derivatives compute normalized cross-correlation for detecting voiced segments, which unfortunately is sensitive to different types of noise. Exploiting the fact that voiced speech is not just periodic but also rich in harmonic, we model voiced segments by adopting harmonic models, which have recently gained considerable attention. In previous work, the parameters of the model were estimated independently for each frame using maximum likelihood criterion. However, since the distribution of harmonic coefficients depend on articulators of speakers, we estimate the model parameters more robustly using a maximum a posteriori criterion. We use the likelihood of voicing, computed from the harmonic model, as an observation probability of an HMM and detect speech using this unsupervised HMM. The one caveat of the harmonic model is that they fail to distinguish speech from other stationary harmonic noise. We rectify this weakness by taking advantage of the non-stationary property of speech. We evaluate our models empirically on a task of detecting speech on a large corpora of everyday speech and demonstrate that these models perform significantly better than standard voice detection algorithm employed in popular tools.