Isaac Bleaman and Ronald Sprouse have published a tutorial on speaker diarization at the Linguistics Methods Hub. The process allows researchers to automatically generate ELAN or Praat files for audio recordings with speech segments marked off on the appropriate speaker tiers — an important first step in the transcription workflow.
Isaac Bleaman and Chaya Nove will be giving a research talk at the 54th annual meeting of the Association for Jewish Studies, held in Boston, December 18-20. Their talk is titled "The Corpus of Spoken Yiddish in Europe: A new resource for language research and pedagogy," and it is part of a panel on "Jewish Corpus Linguistics and Language Documentation."
Gašper Beguš and Alan Zhou (Berkeley Speech and Computation lab alum) published a paper titled "Interpreting Intermediate Convolutional Layers of Generative CNNs Trained on Waveforms" in IEEE/ACM Transactions on Audio, Speech, and Language Processing. The paper is available through Open Access here: https://doi.org/10.1109/TASLP.2022.3209938
On October 3, Gašper Beguš will be giving a colloquium talk at the Yale University Department of Linguistics titled "Deep Phonology: Modeling language from raw acoustic data in a fully unsupervised manner." More information is available here.
Beguš, Gašper and Alan Zhou. 2022. Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data. Proc. Interspeech 2022, 5298-5302. [article] [asynchronous talk] Webber, Jacob J., Samuel K. Lo, and Isaac L. Bleaman. 2022. REYD – The first Yiddish text-to-speech dataset and system. Proc. Interspeech 2022, 2363-2367. [article]
Human speakers encode information into raw speech which is then decoded by the listeners. This complex relationship between encoding (production) and decoding (perception) is often modeled separately. Here, we test how encoding and decoding of lexical semantic information can emerge automatically from raw speech in unsupervised generative deep convolutional networks that combine the production and perception principles of speech. We introduce, to our knowledge, the most challenging objective in unsupervised lexical learning: a network that must learn unique representations for...
Machine learning has been advancing dramatically over the past decade. Most strides are human-based applications due to the availability of large-scale datasets; however, opportunities are ripe to apply this technology to more deeply understand non-human communication. We detail a scientific roadmap for advancing the understanding of communication of whales that can be built further upon as a template to decipher other forms of animal and non-human communication. Sperm whales, with their highly developed neuroanatomical features, cognitive abilities, social structures, and discrete...