Juho Leinonen


Semiautomatic Speech Alignment for Under-Resourced Languages
Juho Leinonen | Niko Partanen | Sami Virpioja | Mikko Kurimo
Proceedings of the Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference

Cross-language forced alignment is a solution for linguists who create speech corpora for very low-resource languages. However, cross-language is an additional challenge making a complex task, forced alignment, even more difficult. We study how linguists can impart domain expertise to the tasks to increase the performance of automatic forced aligners while keeping the time effort still lower than with manual forced alignment. First, we show that speech recognizers have a clear bias in starting the word later than a human annotator, which results in micro-pauses in the results that do not exist in manual alignments, and study which is the best way to automatically remove these silences. Second, we ask the linguists to simplify the task by splitting long interview audios into shorter lengths by providing some manually aligned segments and evaluating the results of this process. We also study how correlated source language performance is to target language performance, since often it is an easier task to find a better source model than to adapt to the target language.


Grapheme-Based Cross-Language Forced Alignment: Results with Uralic Languages
Juho Leinonen | Sami Virpioja | Mikko Kurimo
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

Forced alignment is an effective process to speed up linguistic research. However, most forced aligners are language-dependent, and under-resourced languages rarely have enough resources to train an acoustic model for an aligner. We present a new Finnish grapheme-based forced aligner and demonstrate its performance by aligning multiple Uralic languages and English as an unrelated language. We show that even a simple non-expert created grapheme-to-phoneme mapping can result in useful word alignments.


Service registration chatbot: collecting and comparing dialogues from AMT workers and service’s users
Luca Molteni | Mittul Singh | Juho Leinonen | Katri Leino | Mikko Kurimo | Emanuele Della Valle
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

Crowdsourcing is the go-to solution for data collection and annotation in the context of NLP tasks. Nevertheless, crowdsourced data is noisy by nature; the source is often unknown and additional validation work is performed to guarantee the dataset’s quality. In this article, we compare two crowdsourcing sources on a dialogue paraphrasing task revolving around a chatbot service. We observe that workers hired on crowdsourcing platforms produce lexically poorer and less diverse rewrites than service users engaged voluntarily. Notably enough, on dialogue clarity and optimality, the two paraphrase sources’ human-perceived quality does not differ significantly. Furthermore, for the chatbot service, the combined crowdsourced data is enough to train a transformer-based Natural Language Generation (NLG) system. To enable similar services, we also release tools for collecting data and training the dialogue-act-based transformer-based NLG module.


New Baseline in Automatic Speech Recognition for Northern Sámi
Juho Leinonen | Peter Smit | Sami Virpioja | Mikko Kurimo
Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages