The Donate Speech campaign has so far succeeded in gathering approximately 3600 h of ordinary, colloquial Finnish speech into the Lahjoita puhetta (Donate Speech) corpus. The corpus includes over twenty thousand speakers from all the regions of Finland and from all age brackets. The primary goals of the collection were to create a representative, large-scale resource to study spontaneous spoken Finnish and to accelerate the development of language technology and speech-based services. In this paper, we present the collection process and the collected corpus, and showcase its versatility through multiple use cases. The evaluated use cases include: automatic speech recognition of spontaneous speech, detection of age, gender, dialect and topic and metadata analysis. We provide benchmarks for the use cases, as well downloadable, trained baseline systems with open-source code for reproducibility. One further use case is to verify the metadata and transcripts given in this corpus itself, and to suggest artificial metadata and transcripts for the part of the corpus where it is missing.
In this paper we present a Bidirectional LSTM neural network with a Conditional Random Field layer on top, which utilizes word, character and morph embeddings in order to perform named entity recognition on various Finnish datasets. To overcome the lack of annotated training corpora that arises when dealing with low-resource languages like Finnish, we tried a knowledge transfer technique to transfer tags from Estonian dataset. On the human annotated in-domain Digitoday dataset, out system achieved F1 score of 84.73. On the out-of-domain Wikipedia set we got F1 score of 67.66. In order to see how well the system performs on speech data, we used two datasets containing automatic speech recognition outputs. Since we do not have true labels for those datasets, we used a rule-based system to annotate them and used those annotations as reference labels. On the first dataset which contains Finnish parliament sessions we obtained F1 score of 42.09 and on the second one which contains talks from Yle Pressiklubi we obtained F1 score of 74.54.CCS Concepts: • Computing methodologies → Artificial intelligence; Natural language processing;
Named entities are heavily used in the field of spoken language understanding, which uses speech as an input. The standard way of doing named entity recognition from speech involves a pipeline of two systems, where first the automatic speech recognition system generates the transcripts, and then the named entity recognition system produces the named entity tags from the transcripts. In such cases, automatic speech recognition and named entity recognition systems are trained independently, resulting in the automatic speech recognition branch not being optimized for named entity recognition and vice versa. In this paper, we propose two attention-based approaches for extracting named entities from speech in an end-to-end manner, that show promising results. We compare both attention-based approaches on Finnish, Swedish, and English data sets, underlining their strengths and weaknesses.
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