1,703 research outputs found
Mage - Reactive articulatory feature control of HMM-based parametric speech synthesis
In this paper, we present the integration of articulatory control into MAGE, a framework for realtime and interactive (reactive) parametric speech synthesis using hidden Markov models (HMMs). MAGE is based on the speech synthesis engine from HTS and uses acoustic features (spectrum and f0) to model and synthesize speech. In this work, we replace the standard acoustic models with models combining acoustic and articulatory features, such as tongue, lips and jaw positions. We then use feature-space-switched articulatory-to-acoustic regression matrices to enable us to control the spectral acoustic features by manipulating the articulatory features. Combining this synthesis model with MAGE allows us to interactively and intuitively modify phones synthesized in real time, for example transforming one phone into another, by controlling the configuration of the articulators in a visual display. Index Terms: speech synthesis, reactive, articulators 1
Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed
The motor theory of speech perception holds that we perceive the speech of
another in terms of a motor representation of that speech. However, when we
have learned to recognize a foreign accent, it seems plausible that recognition
of a word rarely involves reconstruction of the speech gestures of the speaker
rather than the listener. To better assess the motor theory and this
observation, we proceed in three stages. Part 1 places the motor theory of
speech perception in a larger framework based on our earlier models of the
adaptive formation of mirror neurons for grasping, and for viewing extensions
of that mirror system as part of a larger system for neuro-linguistic
processing, augmented by the present consideration of recognizing speech in a
novel accent. Part 2 then offers a novel computational model of how a listener
comes to understand the speech of someone speaking the listener's native
language with a foreign accent. The core tenet of the model is that the
listener uses hypotheses about the word the speaker is currently uttering to
update probabilities linking the sound produced by the speaker to phonemes in
the native language repertoire of the listener. This, on average, improves the
recognition of later words. This model is neutral regarding the nature of the
representations it uses (motor vs. auditory). It serve as a reference point for
the discussion in Part 3, which proposes a dual-stream neuro-linguistic
architecture to revisits claims for and against the motor theory of speech
perception and the relevance of mirror neurons, and extracts some implications
for the reframing of the motor theory
Multi-View Multi-Task Representation Learning for Mispronunciation Detection
The disparity in phonology between learner's native (L1) and target (L2)
language poses a significant challenge for mispronunciation detection and
diagnosis (MDD) systems. This challenge is further intensified by lack of
annotated L2 data. This paper proposes a novel MDD architecture that exploits
multiple `views' of the same input data assisted by auxiliary tasks to learn
more distinctive phonetic representation in a low-resource setting. Using the
mono- and multilingual encoders, the model learn multiple views of the input,
and capture the sound properties across diverse languages and accents. These
encoded representations are further enriched by learning articulatory features
in a multi-task setup. Our reported results using the L2-ARCTIC data
outperformed the SOTA models, with a phoneme error rate reduction of 11.13% and
8.60% and absolute F1 score increase of 5.89%, and 2.49% compared to the
single-view mono- and multilingual systems, with a limited L2 dataset.Comment: 5 page
Stages of lexical access
Contains fulltext :
5660.pdf (publisher's version ) (Open Access
Lexical Access Model for Italian -- Modeling human speech processing: identification of words in running speech toward lexical access based on the detection of landmarks and other acoustic cues to features
Modelling the process that a listener actuates in deriving the words intended
by a speaker requires setting a hypothesis on how lexical items are stored in
memory. This work aims at developing a system that imitates humans when
identifying words in running speech and, in this way, provide a framework to
better understand human speech processing. We build a speech recognizer for
Italian based on the principles of Stevens' model of Lexical Access in which
words are stored as hierarchical arrangements of distinctive features (Stevens,
K. N. (2002). "Toward a model for lexical access based on acoustic landmarks
and distinctive features," J. Acoust. Soc. Am., 111(4):1872-1891). Over the
past few decades, the Speech Communication Group at the Massachusetts Institute
of Technology (MIT) developed a speech recognition system for English based on
this approach. Italian will be the first language beyond English to be
explored; the extension to another language provides the opportunity to test
the hypothesis that words are represented in memory as a set of
hierarchically-arranged distinctive features, and reveal which of the
underlying mechanisms may have a language-independent nature. This paper also
introduces a new Lexical Access corpus, the LaMIT database, created and labeled
specifically for this work, that will be provided freely to the speech research
community. Future developments will test the hypothesis that specific acoustic
discontinuities - called landmarks - that serve as cues to features, are
language independent, while other cues may be language-dependent, with powerful
implications for understanding how the human brain recognizes speech.Comment: Submitted to Language and Speech, 202
Remembering with your tongue: articulatory embodiment in memory and speech
Articulatory factors are typically relegated to a peripheral role in theoretical accounts of cognitive function. For example, verbal short-term memory functions are thought to be serviced by dedicated mechanisms that operate on abstract phonological (i.e., non-articulatory) items. An alternative tested here is that memory functions are supported by motor control processes that embody articulatory detail. To provide evidence for this viewpoint, this thesis focuses on the influence of articulatory effort-minimisation processes on memory and speech
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