Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian

Varjokallio, Matti; Virpioja, Sámi; Kurimo, Mikko

doi:10.1016/j.csl.2020.101141

Cited by 4 publications

(2 citation statements)

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“…There are several more modern approaches to speech recognition (Enarvi et al, 2017;Varjokallio et al, 2021) and speech synthesis (Raitio et al, 2008(Raitio et al, , 2014. Although, speech synthesis has not gained much interest in the recent years.…”

Section: Speechmentioning

confidence: 99%

The Current State of Finnish NLP

Hämäläinen¹,

Alnajjar²

2021

Preprint

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There are a lot of tools and resources available for processing Finnish. In this paper, we survey recent papers focusing on Finnish NLP related to many different subcategories of NLP such as parsing, generation, semantics and speech. NLP research is conducted in many different research groups in Finland, and it is frequently the case that NLP tools and models resulting from academic research are made available for others to use on platforms such as Github. TiivistelmäSuomen kielen koneelliseen käsittelyyn on tarjolla paljon valmiita työkaluja ja resursseja.Tässä artikkelissa tarkastelemme viimeaikoina julkaistuja tieteellisiä artikkeleita, joissa keskitytään suomen kielen kieliteknologiaan.Tarkastelemme kieliteknologian eri alaluokkia, kuten jäsentämistä, tuottamista, semantiikkaa ja puheetta. kieliteknologista tutkimusta tehdään Suomessa monissa eri tutkimusryhmissä, ja usein akateemisen tutkimuksen tuloksena tuotetut kieliteknologian työkalut ja mallit julkaistaan muiden käytettäväksi esimerkiksi Githubissa.

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Section: Speechmentioning

confidence: 99%

The Current State of Finnish NLP

Hämäläinen¹,

Alnajjar²

2021

Preprint

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“…In spite of developing a craze to learn English, some problems related to English learning have also attracted the attention of experts and scholars, and they all want to provide support for English learners. The current English research work mainly focuses on self-learning system [19,20], data mining [21], speech recognition [22], and vocabulary standards [23,24], and a specific proportion is shown in Figure 1. The proportion of self-learning system is as high as 33.11%, the proportion of related data mining technology ranks second at 19.21%, and the proportion of speech recognition and vocabulary standards are 10.6% and 9.93%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of spoken English self-study system considering speech knowledge recognition algorithm

Jiang¹

2022

Applied Mathematics and Nonlinear Sciences

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This paper builds and evaluates a self-learning system for spoken English pronunciation suitable for PC mobile terminal or mobile terminal users, and the core technology of speech recognition is built on Hidden Markov Model (HMM), which is used to decode the speech signal in spoken English learning. This paper studies the related speech recognition theory and signal processing technology, builds a comprehensive English self-learning system in more complex situations and more user types and conducts a comprehensive evaluation of the system. The results show that the overall accuracy of the HMM model in the spoken English recognition and evaluation system built in this paper is good, and the accuracy of the input audio for people of all ages is greater than 90%. In the younger population, the accuracy of male speech signals was the highest in both closed space and open space, reaching 98.12% and 96.53%, respectively. In addition, it is observed that the accuracy gradually decreases as more wrong judgements are made on the speech input signal. When the evaluation is poor, the accuracy of the scoring results decreases to 55%, and when there are fewer voice judgement errors, it is in the excellent range, where the accuracy reached 88%.

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