516 research outputs found
Grammaticalization and phonological reidentification in White Hmong
The “dynamic coevolution of meaning and form” of Bybee et al. ( 1994 : 20) has been the subject of significant discussion as regards the languages of Mainland Southeast Asia. However, little work has focused on the mechanisms through which this coevolution occurs when it does surface in these languages. The current work considers phonological reidentification resulting from phonetic reduction in White Hmong (Hmong-Mien, Laos) involving four morphemes, ntshai/ntshe ‘maybe’, saib/seb ‘see if/whether; COMP.CFACT’, puag/pug ‘LOCL;INTS’, and niaj/nej ‘each, every’. These morphemes exhibit an alternation where a rime is phonologically reidentified in a manner consistent with typical phonetic underarticulation patterns, such that an exemplar-model approach (Pierrehumbert 2001 , inter alia) provides a straightforward explanation. Furthermore, the data show that the phonological reidentification patterns found in White Hmong exhibit parallels in other languages in the region, confirming that an areal approach to grammaticalization provides greater descriptive adequacy cross-linguistically as regards this phenomenon
Regularities and Irregularities in Chinese Historical Phonology
With a combination of methodologies from Western and Chinese traditional historical linguistics, this thesis is an attempt to survey and synthetically analyze the major sound changes in Chinese phonological history. It addresses two hypotheses – the Neogrammarian regularity hypothesis and the unidirectionality hypothesis – and tries to question their validity and applicability. Drawing from fourteen types of “regular” and “irregular” processes, the thesis argues that the origins and impetuses of sound change is far from just phonetic environment (“regular” changes) and lexical diffusion (“irregular” changes), and that sound change is not unidirectional because of the existence and significance of fortifying and bi/multidirectional changes. The thesis also examines the sociopolitical aspect of sound change through the discussion of language changes resulting from social, geographical and historical factors, suggesting that the study of sound change should be more interdisciplinary and miscellaneous in order to explain the phenomena more thoroughly and reach a better understanding of how human languages function both synchronically and diachronically
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
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