Spoken command recognition for robotics

In this thesis, I investigate spoken command recognition technology for robotics. While high robustness is expected, the distant and noisy conditions in which the system has to operate make the task very challenging. Unlike commercial systems which all rely on a "wake-up" word to initiate the interaction, the pipeline proposed here directly detect and recognizes commands from the continuous audio stream. In order to keep the task manageable despite low-resource conditions, I propose to focus on a limited set of commands, thus trading off flexibility of the system against robustness. Domain and speaker adaptation strategies based on a multi-task regularization paradigm are first explored. More precisely, two different methods are proposed which rely on a tied loss function which penalizes the distance between the output of several networks. The first method considers each speaker or domain as a task. A canonical task-independent network is jointly trained with task-dependent models, allowing both types of networks to improve by learning from one another. While an improvement of 3.2% on the frame error rate (FER) of the task-independent network is obtained, this only partially carried over to the phone error rate (PER), with 1.5% of improvement. Similarly, a second method explored the parallel training of the canonical network with a privileged model having access to i-vectors. This method proved less effective with only 1.2% of improvement on the FER. In order to make the developed technology more accessible, I also investigated the use of a sequence-to-sequence (S2S) architecture for command classification. The use of an attention-based encoder-decoder model reduced the classification error by 40% relative to a strong convolutional neural network (CNN)-hidden Markov model (HMM) baseline, showing the relevance of S2S architectures in such context. In order to improve the flexibility of the trained system, I also explored strategies for few-shot learning, which allow to extend the set of commands with minimum requirements in terms of data. Retraining a model on the combination of original and new commands, I managed to achieve 40.5% of accuracy on the new commands with only 10 examples for each of them. This scores goes up to 81.5% of accuracy with a larger set of 100 examples per new command. An alternative strategy, based on model adaptation achieved even better scores, with 68.8% and 88.4% of accuracy with 10 and 100 examples respectively, while being faster to train. This high performance is obtained at the expense of the original categories though, on which the accuracy deteriorated. Those results are very promising as the methods allow to easily extend an existing S2S model with minimal resources. Finally, a full spoken command recognition system (named iCubrec) has been developed for the iCub platform. The pipeline relies on a voice activity detection (VAD) system to propose a fully hand-free experience. By segmenting only regions that are likely to contain commands, the VAD module also allows to reduce greatly the computational cost of the pipeline. Command candidates are then passed to the deep neural network (DNN)-HMM command recognition system for transcription. The VoCub dataset has been specifically gathered to train a DNN-based acoustic model for our task. Through multi-condition training with the CHiME4 dataset, an accuracy of 94.5% is reached on VoCub test set. A filler model, complemented by a rejection mechanism based on a confidence score, is finally added to the system to reject non-command speech in a live demonstration of the system

Syväoppiminen puhutun kielen tunnistamisessa

This thesis applies deep learning based classification techniques to identify natural languages from speech. The primary motivation behind this thesis is to implement accurate techniques for segmenting multimedia materials by the languages spoken in them. Several existing state-of-the-art, deep learning based approaches are discussed and a subset of the discussed approaches are selected for quantitative experimentation. The selected model architectures are trained on several well-known spoken language identification datasets containing several different languages. Segmentation granularity varies between models, some supporting input audio lengths of 0.2 seconds, while others require 10 second long input to make a language decision. Results from the thesis experiments show that an unsupervised representation of acoustic units, produced by a deep sequence-to-sequence auto encoder, cannot reach the language identification performance of a supervised representation, produced by a multilingual phoneme recognizer. Contrary to most existing results, in this thesis, acoustic-phonetic language classifiers trained on labeled spectral representations outperform phonotactic classifiers trained on bottleneck features of a multilingual phoneme recognizer. More work is required, using transcribed datasets and automatic speech recognition techniques, to investigate why phoneme embeddings did not outperform simple, labeled spectral features. While an accurate online language segmentation tool for multimedia materials could not be constructed, the work completed in this thesis provides several insights for building feasible, modern spoken language identification systems. As a side-product of the experiments performed during this thesis, a free open source spoken language identification software library called "lidbox" was developed, allowing future experiments to begin where the experiments of this thesis end.Tämä diplomityö keskittyy soveltamaan syviä neuroverkkomalleja luonnollisten kielien automaattiseen tunnistamiseen puheesta. Tämän työn ensisijainen tavoite on toteuttaa tarkka menetelmä multimediamateriaalien ositteluun niissä esiintyvien puhuttujen kielien perusteella. Työssä tarkastellaan useampaa jo olemassa olevaa neuroverkkoihin perustuvaa lähestymistapaa, joista valitaan alijoukko tarkempaan tarkasteluun, kvantitatiivisten kokeiden suorittamiseksi. Valitut malliarkkitehtuurit koulutetaan käyttäen eri puhetietokantoja, sisältäen useampia eri kieliä. Kieliosittelun hienojakoisuus vaihtelee käytettyjen mallien mukaan, 0,2 sekunnista 10 sekuntiin, riippuen kuinka pitkän aikaikkunan perusteella malli pystyy tuottamaan kieliennusteen. Diplomityön aikana suoritetut kokeet osoittavat, että sekvenssiautoenkoodaajalla ohjaamattomasti löydetty puheen diskreetti akustinen esitysmuoto ei ole riittävä kielen tunnistamista varten, verrattuna foneemitunnistimen tuottamaan, ohjatusti opetettuun foneemiesitysmuotoon. Tässä työssä havaittiin, että akustisfoneettiset kielentunnistusmallit saavuttavat korkeamman kielentunnistustarkkuuden kuin foneemiesitysmuotoa käyttävät kielentunnistusmallit, mikä eroaa monista kirjallisuudessa esitetyistä tuloksista. Diplomityön tutkimuksia on jatkettava, esimerkiksi litteroituja puhetietokantoja ja puheentunnistusmenetelmiä käyttäen, jotta pystyttäisiin selittämään miksi foneemimallin tuottamalla esitysmuodolla ei saatu parempia tuloksia kuin yksinkertaisemmalla, taajuusspektrin esitysmuodolla. Tämän työn aikana puhutun kielen tunnistaminen osoittautui huomattavasti haasteellisemmaksi kuin mitä työn alussa oli arvioitu, eikä työn aikana onnistuttu toteuttamaan tarpeeksi tarkkaa multimediamateriaalien kielienosittelumenetelmää. Tästä huolimatta, työssä esitetyt lähestymistavat tarjoavat toimivia käytännön menetelmiä puhutun kielen tunnistamiseen tarkoitettujen, modernien järjestelmien rakentamiseksi. Tämän diplomityön sivutuotteena syntyi myös puhutun kielen tunnistamiseen tarkoitettu avoimen lähdekoodin kirjasto nimeltä "lidbox", jonka ansiosta tämän työn kvantitatiivisia kokeita voi jatkaa siitä, mihin ne tämän työn päätteeksi jäivät

Voice Activity Detection and Garbage Modelling for a Mobile Automatic Speech Recognition Application

Recently, state-of-the-art automatic speech recognition systems are used in various industries all over the world. Most of them are using a customized version of speech recognition system. The need for different versions arise due to different speech commands, lexicon, language and distinct work environment. It is essential for a speech recognizer to provide accurate and precise outputs in every working environment. However, the performance of a speech recognizer degrades quickly when noise intermingles with a work environment and also when out-of-vocabulary (OOV) words are spoken to the speech recognizer. This thesis consists of three different tasks which improve an automatic speech recognition application for mobile devices. The three tasks include building of a new acoustic model, improving the current voice activity detection and garbage modelling of OOV words. In this thesis, firstly, a Finnish acoustic model is trained for a company called Devoca Oy. The training data was recorded from different warehouse environments to improve the real-world speech recognition accuracy. Secondly, the Gammatone and Gabor features are extracted from the input speech frame to improve the voice activity detection (VAD). These features are applied to the VAD decision module of Pocketsphinx and a new neural-network classifier, to be classified as speech or non-speech. Lastly, a garbage model is developed for the OOV words. This model recognizes the words from outside the grammar and marks them as unknown on the application interface. This thesis evaluates the success of these three tasks with Finnish audio database and reports the overall improvement in the word error rate

Speaker Recognition on Mobile Phone

Tato práce se zaměřuje na implementaci počítačového systému rozpoznávání řečníka do prostředí mobilního telefonu. Je zde popsán princip, funkce, a implementace rozpoznávače na mobilním telefonu Nokia N900.This work aims to port Speaker Identification System (SID) to the mobile device / mobile phone. We will describe basic principles, function and implementation of speaker identification system on Nokia N900 mobile phone.

Automatic Video Captioning using Deep Neural Network

Video understanding has become increasingly important as surveillance, social, and informational videos weave themselves into our everyday lives. Video captioning offers a simple way to summarize, index, and search the data. Most video captioning models utilize a video encoder and captioning decoder framework. Hierarchical encoders can abstractly capture clip level temporal features to represent a video, but the clips are at fixed time steps. This thesis research introduces two models: a hierarchical model with steered captioning, and a Multi-stream Hierarchical Boundary model. The steered captioning model is the first attention model to smartly guide an attention model to appropriate locations in a video by using visual attributes. The Multi-stream Hierarchical Boundary model combines a fixed hierarchy recurrent architecture with a soft hierarchy layer by using intrinsic feature boundary cuts within a video to define clips. This thesis also introduces a novel parametric Gaussian attention which removes the restriction of soft attention techniques which require fixed length video streams. By carefully incorporating Gaussian attention in designated layers, the proposed models demonstrate state-of-the-art video captioning results on recent datasets

Voice activity detection based on density ratio estimation and system combination

Abstract-We propose a robust voice activity detection (VAD) based on density ratio estimation. In highly noisy environments, the likelihood ratio test (LRT) is effective. Conventional LRT estimates both speech and noise models, calculates the likelihood of each model, and uses ratios of such likelihood to detect speech. However, in LRT, the likelihood ratio of speech and noise models is required, whereas likelihood of individual models is not necessarily required. The framework of the density ratio estimation models likelihood ratio functions by a kernel and directly generates a likelihood ratio. Applying density ratio estimation to VAD requires that feature selection and noise adaptation must be considered. This is because the density ratio estimation constrains the shape of the likelihood ratio functions and speech is dynamic. This paper addresses these problems. To improve accuracy, the proposed method is combined with conventional LRT. Experimental results using CENSREC-1-C show that the proposed method is more effective than conventional methods, especially in non-stationary noisy environments