115 research outputs found
Speech Recognition Challenge in the Wild: Arabic MGB-3
This paper describes the Arabic MGB-3 Challenge - Arabic Speech Recognition
in the Wild. Unlike last year's Arabic MGB-2 Challenge, for which the
recognition task was based on more than 1,200 hours broadcast TV news
recordings from Aljazeera Arabic TV programs, MGB-3 emphasises dialectal Arabic
using a multi-genre collection of Egyptian YouTube videos. Seven genres were
used for the data collection: comedy, cooking, family/kids, fashion, drama,
sports, and science (TEDx). A total of 16 hours of videos, split evenly across
the different genres, were divided into adaptation, development and evaluation
data sets. The Arabic MGB-Challenge comprised two tasks: A) Speech
transcription, evaluated on the MGB-3 test set, along with the 10 hour MGB-2
test set to report progress on the MGB-2 evaluation; B) Arabic dialect
identification, introduced this year in order to distinguish between four major
Arabic dialects - Egyptian, Levantine, North African, Gulf, as well as Modern
Standard Arabic. Two hours of audio per dialect were released for development
and a further two hours were used for evaluation. For dialect identification,
both lexical features and i-vector bottleneck features were shared with
participants in addition to the raw audio recordings. Overall, thirteen teams
submitted ten systems to the challenge. We outline the approaches adopted in
each system, and summarise the evaluation results
ArTST: Arabic Text and Speech Transformer
We present ArTST, a pre-trained Arabic text and speech transformer for
supporting open-source speech technologies for the Arabic language. The model
architecture follows the unified-modal framework, SpeechT5, that was recently
released for English, and is focused on Modern Standard Arabic (MSA), with
plans to extend the model for dialectal and code-switched Arabic in future
editions. We pre-trained the model from scratch on MSA speech and text data,
and fine-tuned it for the following tasks: Automatic Speech Recognition (ASR),
Text-To-Speech synthesis (TTS), and spoken dialect identification. In our
experiments comparing ArTST with SpeechT5, as well as with previously reported
results in these tasks, ArTST performs on a par with or exceeding the current
state-of-the-art in all three tasks. Moreover, we find that our pre-training is
conducive for generalization, which is particularly evident in the low-resource
TTS task. The pre-trained model as well as the fine-tuned ASR and TTS models
are released for research use.Comment: 11 pages, 1 figure, SIGARAB ArabicNLP 202
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
Multi-dialect Arabic broadcast speech recognition
Dialectal Arabic speech research suffers from the lack of labelled resources and
standardised orthography. There are three main challenges in dialectal Arabic
speech recognition: (i) finding labelled dialectal Arabic speech data, (ii) training
robust dialectal speech recognition models from limited labelled data and (iii)
evaluating speech recognition for dialects with no orthographic rules. This thesis
is concerned with the following three contributions:
Arabic Dialect Identification: We are mainly dealing with Arabic speech
without prior knowledge of the spoken dialect. Arabic dialects could be sufficiently
diverse to the extent that one can argue that they are different languages
rather than dialects of the same language. We have two contributions:
First, we use crowdsourcing to annotate a multi-dialectal speech corpus collected
from Al Jazeera TV channel. We obtained utterance level dialect labels for 57
hours of high-quality consisting of four major varieties of dialectal Arabic (DA),
comprised of Egyptian, Levantine, Gulf or Arabic peninsula, North African or
Moroccan from almost 1,000 hours. Second, we build an Arabic dialect identification
(ADI) system. We explored two main groups of features, namely acoustic
features and linguistic features. For the linguistic features, we look at a wide
range of features, addressing words, characters and phonemes. With respect to
acoustic features, we look at raw features such as mel-frequency cepstral coefficients
combined with shifted delta cepstra (MFCC-SDC), bottleneck features and
the i-vector as a latent variable. We studied both generative and discriminative
classifiers, in addition to deep learning approaches, namely deep neural network
(DNN) and convolutional neural network (CNN). In our work, we propose Arabic
as a five class dialect challenge comprising of the previously mentioned four
dialects as well as modern standard Arabic.
Arabic Speech Recognition: We introduce our effort in building Arabic automatic
speech recognition (ASR) and we create an open research community
to advance it. This section has two main goals: First, creating a framework for
Arabic ASR that is publicly available for research. We address our effort in building
two multi-genre broadcast (MGB) challenges. MGB-2 focuses on broadcast
news using more than 1,200 hours of speech and 130M words of text collected
from the broadcast domain. MGB-3, however, focuses on dialectal multi-genre
data with limited non-orthographic speech collected from YouTube, with special
attention paid to transfer learning. Second, building a robust Arabic ASR system
and reporting a competitive word error rate (WER) to use it as a potential
benchmark to advance the state of the art in Arabic ASR. Our overall system is
a combination of five acoustic models (AM): unidirectional long short term memory
(LSTM), bidirectional LSTM (BLSTM), time delay neural network (TDNN),
TDNN layers along with LSTM layers (TDNN-LSTM) and finally TDNN layers
followed by BLSTM layers (TDNN-BLSTM). The AM is trained using purely
sequence trained neural networks lattice-free maximum mutual information (LFMMI).
The generated lattices are rescored using a four-gram language model
(LM) and a recurrent neural network with maximum entropy (RNNME) LM.
Our official WER is 13%, which has the lowest WER reported on this task.
Evaluation: The third part of the thesis addresses our effort in evaluating dialectal
speech with no orthographic rules. Our methods learn from multiple
transcribers and align the speech hypothesis to overcome the non-orthographic
aspects. Our multi-reference WER (MR-WER) approach is similar to the BLEU
score used in machine translation (MT). We have also automated this process
by learning different spelling variants from Twitter data. We mine automatically
from a huge collection of tweets in an unsupervised fashion to build more than
11M n-to-m lexical pairs, and we propose a new evaluation metric: dialectal
WER (WERd). Finally, we tried to estimate the word error rate (e-WER) with
no reference transcription using decoding and language features. We show that
our word error rate estimation is robust for many scenarios with and without the
decoding features
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
We present a structured overview of adaptation algorithms for neural
network-based speech recognition, considering both hybrid hidden Markov model /
neural network systems and end-to-end neural network systems, with a focus on
speaker adaptation, domain adaptation, and accent adaptation. The overview
characterizes adaptation algorithms as based on embeddings, model parameter
adaptation, or data augmentation. We present a meta-analysis of the performance
of speech recognition adaptation algorithms, based on relative error rate
reductions as reported in the literature.Comment: Submitted to IEEE Open Journal of Signal Processing. 30 pages, 27
figure
Advancing the Egyptian Public Broadcaster to Compete in the Digital Era
In light of the dramatic technological developments the media industry is witnessing in Egypt and the region, the Egyptian public broadcaster seems missing from the scene. While many privately owned channels are offering some form of online catch-up service, the first Egyptian broadcaster is still very weak online with some presence on YouTube that does not meet the opportunities other broadcasters are benefiting from in the region as well as globally. Through a series of in-depth interviews, the study aims at closely examining the strengths and weaknesses of the current digital presence of the Egyptian public broadcaster and providing recommendations on how it can be enabled to compete with privately owned competitors. Having the legacy in the realm of linear TV in the Middle East and currently undergoing a renovation process in terms of content that is planned strongly compete within the upcoming period, a strong digital presence is a must and a key factor to succeeding in reaching such a goal. The study will identify the different formats and business models for existing competition and formulate the digital strategy that would best suit the current state of the public broadcaster
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