There is growing interest in ASR systems that can recognize phones in a language-independent fashion. There is additionally interest in building language technologies for low-resource and endangered languages. However, there is a paucity of realistic data that can be used to test such systems and technologies. This paper presents a publicly available, phonetically transcribed corpus of 2255 utterances (words and short phrases) in the endangered Tangkhulic language East Tusom (no ISO 639-3 code), a Tibeto-Burman language variety spoken mostly in India. Because the dataset is transcribed in terms of phones, rather than phonemes, it is a better match for universal phone recognition systems than many larger (phonemically transcribed) datasets. This paper describes the dataset and the methodology used to produce it. It further presents basic benchmarks of state-of-the-art universal phone recognition systems on the dataset as baselines for future experiments.

Transcribing speech is an important part of language documentation, yet speech recognition technology has not been widely harnessed to aid linguists. We explore the use of a neural network architecture with the connectionist temporal classification loss function for phonemic and tonal transcription in a language documentation setting. In this framework, we explore jointly modelling phonemes and tones versus modelling them separately, and assess the importance of pitch information versus phonemic context for tonal prediction. Experiments on two tonal languages, Yongning Na and Eastern Chatino, show the changes in recognition performance as training data is scaled from 10 minutes up to 50 minutes for Chatino, and up to 224 minutes for Na. We discuss the findings from incorporating this technology into the linguistic workflow for documenting Yongning Na, which show the method's promise in improving efficiency, minimizing typographical errors, and maintaining the transcription's faithfulness to the acoustic signal, while highlighting phonetic and phonemic facts for linguistic consideration.

Machine learning has revolutionised speech technologies for major world languages, but these technologies have generally not been available for the roughly 4,000 languages with populations of fewer than 10,000 speakers. This paper describes the development of ELPIS, a pipeline which language documentation workers with minimal computational experience can use to build their own speech recognition models, resulting in models being built for 16 languages from the Asia-Pacific region. ELPIS puts machine learning speech technologies within reach of people working with languages with scarce data, in a scalable way. This is impactful since it enables language communities to cross the digital divide, and speeds up language documentation. Complete automation of the process is not feasible for languages with small quantities of data and potentially large vocabularies. Hence our goal is not full automation, but rather to make a practical and effective workflow that integrates machine learning technologies.

Slides for a paper presented at the 2023 symposium "Tikal and its Wooden Memory" at the Museum Der Kulturen, Basel, Switzerland. October 13, 2023. This talk looks at the narrative structures of the wooden lintels in two of Tikal's major temples, proposing that they were placed in their original setting above or in proximity to the captured effigies of foreign deities (Yamanajaw and Waxaklajuun Ubaah Chan in Temple I, and the patron deities of El Peru and Naranjo in Temple IV). While the physical effigies were likely place within the small confines of the structures, the narratives also emphasize the Tikal rulers' ritual ability to embody or "impersonate" these deities. I argue that the lofty temples were settings for the display and presentation of the deities and/or the victorious deified rulers, and Temple IV at least may have been intentionally built too be oriented directly toward Tikal's enemy, Naranjo, to the east.

Daniel Kahneman, uno de los pensadores más importantes del mundo, recibió el premio Nobel de Economía por su trabajo pionero en psicología sobre el modelo racional de la toma de decisiones. Sus ideas han tenido un profundo impacto en campos tan diversos como la economía, la medicina o la política, pero hasta ahora no había reunido la obra de su vida en un libro. En Pensar rápido, pensar despacio, un éxito internacional, Kahneman nos ofrece una revolucionaria perspectiva del cerebro y explica los dos sistemas que modelan cómo pensamos. El sistema 1 es rápido, intuitivo y emocional, mientras que el sistema 2 es más lento, deliberativo y lógico. Kahneman expone la extraordinaria capacidad (y también los errores y los sesgos) del pensamiento rápido, y revela la duradera influencia de las impresiones intuitivas sobre nuestro pensamiento y nuestra conducta. El impacto de la aversión a la pérdida y el exceso de confianza en las estrategias empresariales, la dificultad de predecir lo que nos hará felices en el futuro, el reto de enmarcar adecuadamente los riesgos en el trabajo y en el hogar, el profundo efecto de los sesgos cognitivos sobre todo lo que hacemos, desde jugar en la Bolsa hasta planificar las vacaciones; todo esto solo puede ser comprendido si entendemos el funcionamiento conjunto de los dos sistemas a la hora de formular nuestros juicios y decisiones. Al implicar al lector en una animada reflexión sobre cómo pensamos, Kahneman consigue revelar cuándo podemos confiar en nuestras intuiciones y cuándo no, y de qué modo podemos aprovechar los beneficios del pensamiento lento. Además, ofrece enseñanzas prácticas e iluminadoras sobre cómo se adoptan decisiones en la vida profesional o personal, y sobre cómo podemos usar distintas técnicas para protegernos de los fallos mentales que nos crean problemas. Pensar rápido, pensar despacio cambiará para siempre nuestra manera de pensar sobre cómo pensamos. www.lectulandia.com-Página 2