2,392 research outputs found
Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
Using supporting backchannel (BC) cues can make human-computer interaction
more social. BCs provide a feedback from the listener to the speaker indicating
to the speaker that he is still listened to. BCs can be expressed in different
ways, depending on the modality of the interaction, for example as gestures or
acoustic cues. In this work, we only considered acoustic cues. We are proposing
an approach towards detecting BC opportunities based on acoustic input features
like power and pitch. While other works in the field rely on the use of a
hand-written rule set or specialized features, we made use of artificial neural
networks. They are capable of deriving higher order features from input
features themselves. In our setup, we first used a fully connected feed-forward
network to establish an updated baseline in comparison to our previously
proposed setup. We also extended this setup by the use of Long Short-Term
Memory (LSTM) networks which have shown to outperform feed-forward based setups
on various tasks. Our best system achieved an F1-Score of 0.37 using power and
pitch features. Adding linguistic information using word2vec, the score
increased to 0.39
Continuous Interaction with a Virtual Human
Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access
Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour
Rapport, the close and harmonious relationship in which interaction partners
are "in sync" with each other, was shown to result in smoother social
interactions, improved collaboration, and improved interpersonal outcomes. In
this work, we are first to investigate automatic prediction of low rapport
during natural interactions within small groups. This task is challenging given
that rapport only manifests in subtle non-verbal signals that are, in addition,
subject to influences of group dynamics as well as inter-personal
idiosyncrasies. We record videos of unscripted discussions of three to four
people using a multi-view camera system and microphones. We analyse a rich set
of non-verbal signals for rapport detection, namely facial expressions, hand
motion, gaze, speaker turns, and speech prosody. Using facial features, we can
detect low rapport with an average precision of 0.7 (chance level at 0.25),
while incorporating prior knowledge of participants' personalities can even
achieve early prediction without a drop in performance. We further provide a
detailed analysis of different feature sets and the amount of information
contained in different temporal segments of the interactions.Comment: 12 pages, 6 figure
Sperry Univac speech communications technology
Technology and systems for effective verbal communication with computers were developed. A continuous speech recognition system for verbal input, a word spotting system to locate key words in conversational speech, prosodic tools to aid speech analysis, and a prerecorded voice response system for speech output are described
Modeling Speaker-Listener Interaction for Backchannel Prediction
We present our latest findings on backchannel modeling novelly motivated by
the canonical use of the minimal responses Yeah and Uh-huh in English and their
correspondent tokens in German, and the effect of encoding the speaker-listener
interaction. Backchanneling theories emphasize the active and continuous role
of the listener in the course of the conversation, their effects on the
speaker's subsequent talk, and the consequent dynamic speaker-listener
interaction. Therefore, we propose a neural-based acoustic backchannel
classifier on minimal responses by processing acoustic features from the
speaker speech, capturing and imitating listeners' backchanneling behavior, and
encoding speaker-listener interaction. Our experimental results on the
Switchboard and GECO datasets reveal that in almost all tested scenarios the
speaker or listener behavior embeddings help the model make more accurate
backchannel predictions. More importantly, a proper interaction encoding
strategy, i.e., combining the speaker and listener embeddings, leads to the
best performance on both datasets in terms of F1-score.Comment: Published in IWSDS 202
Nonparallel Emotional Speech Conversion
We propose a nonparallel data-driven emotional speech conversion method. It
enables the transfer of emotion-related characteristics of a speech signal
while preserving the speaker's identity and linguistic content. Most existing
approaches require parallel data and time alignment, which is not available in
most real applications. We achieve nonparallel training based on an
unsupervised style transfer technique, which learns a translation model between
two distributions instead of a deterministic one-to-one mapping between paired
examples. The conversion model consists of an encoder and a decoder for each
emotion domain. We assume that the speech signal can be decomposed into an
emotion-invariant content code and an emotion-related style code in latent
space. Emotion conversion is performed by extracting and recombining the
content code of the source speech and the style code of the target emotion. We
tested our method on a nonparallel corpora with four emotions. Both subjective
and objective evaluations show the effectiveness of our approach.Comment: Published in INTERSPEECH 2019, 5 pages, 6 figures. Simulation
available at http://www.jian-gao.org/emoga
The L2F - UPC Speaker Recognition System for NIST SRE 2010
This document describes the joint submission of the
INESC-ID’s Spoken Language Systems Laboratory (L
2
F) and
the TALP Research Center from the Technical University of
Catalonia (UPC) to the 2010 NIST Speaker Recognition evaluation. The L2F-UPC primary system is composed by the fusion
of five individual sub-systems. Speaker recognition results have
been submitted only for the core-core conditionPostprint (published version
Efficient, end-to-end and self-supervised methods for speech processing and generation
Deep learning has affected the speech processing and generation fields in many directions. First, end-to-end architectures allow the direct injection and synthesis of waveform samples. Secondly, the exploration of efficient solutions allow to implement these systems in computationally restricted environments, like smartphones. Finally, the latest trends exploit audio-visual data with least supervision. In this thesis these three directions are explored.
Firstly, we propose the use of recent pseudo-recurrent structures, like self-attention models and quasi-recurrent networks, to build acoustic models for text-to-speech. The proposed system, QLAD, turns out to synthesize faster on CPU and GPU than its recurrent counterpart whilst preserving the good synthesis quality level, which is competitive with state of the art vocoder-based models.
Then, a generative adversarial network is proposed for speech enhancement, named SEGAN. This model works as a speech-to-speech conversion system in time-domain, where a single inference operation is needed for all samples to operate through a fully convolutional structure. This implies an increment in modeling efficiency with respect to other existing models, which are auto-regressive and also work in time-domain. SEGAN achieves prominent results in noise supression and preservation of speech naturalness and intelligibility when compared to the other classic and deep regression based systems. We also show that SEGAN is efficient in transferring its operations to new languages and noises. A SEGAN trained for English performs similarly to this language on Catalan and Korean with only 24 seconds of adaptation data. Finally, we unveil the generative capacity of the model to recover signals from several distortions. We hence propose the concept of generalized speech enhancement. First, the model proofs to be effective to recover voiced speech from whispered one. Then the model is scaled up to solve other distortions that require a recomposition of damaged parts of the signal, like extending the bandwidth or recovering lost temporal sections, among others. The model improves by including additional acoustic losses in a multi-task setup to impose a relevant perceptual weighting on the generated result. Moreover, a two-step training schedule is also proposed to stabilize the adversarial training after the addition of such losses, and both components boost SEGAN's performance across distortions.Finally, we propose a problem-agnostic speech encoder, named PASE, together with the framework to train it. PASE is a fully convolutional network that yields compact representations from speech waveforms. These representations contain abstract information like the speaker identity, the prosodic features or the spoken contents. A self-supervised framework is also proposed to train this encoder, which suposes a new step towards unsupervised learning for speech processing. Once the encoder is trained, it can be exported to solve different tasks that require speech as input. We first explore the performance of PASE codes to solve speaker recognition, emotion recognition and speech recognition. PASE works competitively well compared to well-designed classic features in these tasks, specially after some supervised adaptation. Finally, PASE also provides good descriptors of identity for multi-speaker modeling in text-to-speech, which is advantageous to model novel identities without retraining the model.L'aprenentatge profund ha afectat els camps de processament i generació de la parla en vàries direccions. Primer, les arquitectures fi-a-fi permeten la injecció i síntesi de mostres temporals directament. D'altra banda, amb l'exploració de solucions eficients permet l'aplicació d'aquests sistemes en entorns de computació restringida, com els telèfons intel·ligents. Finalment, les darreres tendències exploren les dades d'àudio i veu per derivar-ne representacions amb la mínima supervisió. En aquesta tesi precisament s'exploren aquestes tres direccions. Primer de tot, es proposa l'ús d'estructures pseudo-recurrents recents, com els models d’auto atenció i les xarxes quasi-recurrents, per a construir models acústics text-a-veu. Així, el sistema QLAD proposat en aquest treball sintetitza més ràpid en CPU i GPU que el seu homòleg recurrent, preservant el mateix nivell de qualitat de síntesi, competitiu amb l'estat de l'art en models basats en vocoder. A continuació es proposa un model de xarxa adversària generativa per a millora de veu, anomenat SEGAN. Aquest model fa conversions de veu-a-veu en temps amb una sola operació d'inferència sobre una estructura purament convolucional. Això implica un increment en l'eficiència respecte altres models existents auto regressius i que també treballen en el domini temporal. La SEGAN aconsegueix resultats prominents d'extracció de soroll i preservació de la naturalitat i la intel·ligibilitat de la veu comparat amb altres sistemes clàssics i models regressius basats en xarxes neuronals profundes en espectre. També es demostra que la SEGAN és eficient transferint les seves operacions a nous llenguatges i sorolls. Així, un model SEGAN entrenat en Anglès aconsegueix un rendiment comparable a aquesta llengua quan el transferim al català o al coreà amb només 24 segons de dades d'adaptació. Finalment, explorem l'ús de tota la capacitat generativa del model i l’apliquem a recuperació de senyals de veu malmeses per vàries distorsions severes. Això ho anomenem millora de la parla generalitzada. Primer, el model demostra ser efectiu per a la tasca de recuperació de senyal sonoritzat a partir de senyal xiuxiuejat. Posteriorment, el model escala a poder resoldre altres distorsions que requereixen una reconstrucció de parts del senyal que s’han malmès, com extensió d’ample de banda i recuperació de seccions temporals perdudes, entre d’altres. En aquesta última aplicació del model, el fet d’incloure funcions de pèrdua acústicament rellevants incrementa la naturalitat del resultat final, en una estructura multi-tasca que prediu característiques acústiques a la sortida de la xarxa discriminadora de la nostra GAN. També es proposa fer un entrenament en dues etapes del sistema SEGAN, el qual mostra un increment significatiu de l’equilibri en la sinèrgia adversària i la qualitat generada finalment després d’afegir les funcions acústiques. Finalment, proposem un codificador de veu agnòstic al problema, anomenat PASE, juntament amb el conjunt d’eines per entrenar-lo. El PASE és un sistema purament convolucional que crea representacions compactes de trames de veu. Aquestes representacions contenen informació abstracta com identitat del parlant, les característiques prosòdiques i els continguts lingüístics. També es proposa un entorn auto-supervisat multi-tasca per tal d’entrenar aquest sistema, el qual suposa un avenç en el terreny de l’aprenentatge no supervisat en l’àmbit del processament de la parla. Una vegada el codificador esta entrenat, es pot exportar per a solventar diferents tasques que requereixin tenir senyals de veu a l’entrada. Primer explorem el rendiment d’aquest codificador per a solventar tasques de reconeixement del parlant, de l’emoció i de la parla, mostrant-se efectiu especialment si s’ajusta la representació de manera supervisada amb un conjunt de dades d’adaptació
- …