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.

Speaking is a fundamental way of communication, developed at a young age. Unfortunately, some children with speech sound disorder struggle to acquire this skill, hindering their ability to communicate efficiently. Speech therapies, which could aid these children in speech acquisition, greatly rely on speech practice trials and accurate feedback about their pronunciations. To enable home therapy and lessen the burden on speech-language pathologists, we need a highly accurate and automatic way of assessing the quality of speech uttered by young children. Our work focuses on exploring the applicability of state-of-the-art self-supervised, deep acoustic models, mainly wav2vec2, for this task. The empirical results highlight that these self-supervised models are superior to traditional approaches and close the gap between machine and human performance.

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The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years. Unfortunately, there is no central database for this information and it is too voluminous to assess manually. We describe our efforts to enable systematic research over US environmental policy by extracting and organizing metadata from the text of NEPA documents. Our contributions include collecting more than 40,000 NEPA-related documents, and evaluating rule-based baselines that establish the difficulty of three important tasks: identifying lead agencies, aligning document versions, and detecting reused text.

Unlike traditional (hybrid) Automatic Speech Recognition (ASR), end-to-end ASR systems simplify the training procedure by directly mapping acoustic features to sequences of graphemes or characters, thereby eliminating the need for specialized acoustic, language, or pronunciation models. However, one drawback of end-to-end ASR systems is that they require more training data than conventional ASR systems to achieve similar word error rate (WER). This makes it difficult to develop ASR systems for tasks where transcribed target data is limited such as developing ASR for Second Language (L2) speakers of Swedish. Nonetheless, recent advancements in selfsupervised acoustic learning, manifested in wav2vec models [1, 2, 3], leverage the available untranscribed speech data to provide compact acoustic representation that can achieve low WER when incorporated in end-to-end systems. To this end, we experiment with several monolingual and cross-lingual selfsupervised acoustic models to develop end-to-end ASR system for L2 Swedish. Even though our test is very small, it indicates that these systems are competitive in performance with traditional ASR pipeline. Our best model seems to reduce the WER by 7% relative to our traditional ASR baseline trained on the same target data.

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We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings shows competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings.