13,598 research outputs found
Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory Input
Articulatory information has been shown to be effective in improving the
performance of HMM-based and DNN-based text-to-speech synthesis. Speech
synthesis research focuses traditionally on text-to-speech conversion, when the
input is text or an estimated linguistic representation, and the target is
synthesized speech. However, a research field that has risen in the last decade
is articulation-to-speech synthesis (with a target application of a Silent
Speech Interface, SSI), when the goal is to synthesize speech from some
representation of the movement of the articulatory organs. In this paper, we
extend traditional (vocoder-based) DNN-TTS with articulatory input, estimated
from ultrasound tongue images. We compare text-only, ultrasound-only, and
combined inputs. Using data from eight speakers, we show that that the combined
text and articulatory input can have advantages in limited-data scenarios,
namely, it may increase the naturalness of synthesized speech compared to
single text input. Besides, we analyze the ultrasound tongue recordings of
several speakers, and show that misalignments in the ultrasound transducer
positioning can have a negative effect on the final synthesis performance.Comment: accepted at SSW11 (11th Speech Synthesis Workshop
Control concepts for articulatory speech synthesis
We present two concepts for the generation of gestural scores to control an articulatory speech synthesizer. Gestural scores are the common input to the synthesizer and constitute an or- ganized pattern of articulatory gestures. The first concept gen- erates the gestures for an utterance using the phonetic transcrip- tions, phone durations, and intonation commands predicted by the Bonn Open Synthesis System (BOSS) from an arbitrary in- put text. This concept extends the synthesizerto a text-to-speech synthesis system. The idea of the second concept is to use tim- ing informationextracted from ElectromagneticArticulography signals to generate the articulatory gestures. Therefore, it is a concept for the re-synthesis of natural utterances. Finally, ap- plication prospects for the presented synthesizer are discussed
The Unsupervised Acquisition of a Lexicon from Continuous Speech
We present an unsupervised learning algorithm that acquires a
natural-language lexicon from raw speech. The algorithm is based on the optimal
encoding of symbol sequences in an MDL framework, and uses a hierarchical
representation of language that overcomes many of the problems that have
stymied previous grammar-induction procedures. The forward mapping from symbol
sequences to the speech stream is modeled using features based on articulatory
gestures. We present results on the acquisition of lexicons and language models
from raw speech, text, and phonetic transcripts, and demonstrate that our
algorithm compares very favorably to other reported results with respect to
segmentation performance and statistical efficiency.Comment: 27 page technical repor
Ultrasound-Based Silent Speech Interface Built on a Continuous Vocoder
Recently it was shown that within the Silent Speech Interface (SSI) field,
the prediction of F0 is possible from Ultrasound Tongue Images (UTI) as the
articulatory input, using Deep Neural Networks for articulatory-to-acoustic
mapping. Moreover, text-to-speech synthesizers were shown to produce higher
quality speech when using a continuous pitch estimate, which takes non-zero
pitch values even when voicing is not present. Therefore, in this paper on
UTI-based SSI, we use a simple continuous F0 tracker which does not apply a
strict voiced / unvoiced decision. Continuous vocoder parameters (ContF0,
Maximum Voiced Frequency and Mel-Generalized Cepstrum) are predicted using a
convolutional neural network, with UTI as input. The results demonstrate that
during the articulatory-to-acoustic mapping experiments, the continuous F0 is
predicted with lower error, and the continuous vocoder produces slightly more
natural synthesized speech than the baseline vocoder using standard
discontinuous F0.Comment: 5 pages, 3 figures, accepted for publication at Interspeech 201
- …