Forced alignment for Nordic languages: Rapidly constructing a high-quality prototype

2021 ◽  
pp. 1-27
Author(s):  
Nathan J. Young ◽  
Michael McGarrah

Abstract We propose a rapid adaptation of FAVE-Align to the Nordic languages, and we offer our own adaptation to Swedish as a template. This study is motivated by the fact that researchers of lesser-studied languages often neither have sufficient speech material nor sufficient time to train a forced aligner. Faced with a similar problem, we made a limited number of surface changes to FAVE-Align so that it – along with its original hidden Markov models for English – could be used on Stockholm Swedish. We tested the performance of this prototype on the three main sociolects of Stockholm Swedish and found that read-aloud alignments met all of the minimal benchmarks set by the literature. Spontaneous-speech alignments met three of the four minimal benchmarks. We conclude that an adaptation such as ours would especially suit laboratory experiments in Nordic phonetics that rely on elicited speech.

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi

Author(s):  
M. Vidyasagar

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.


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