Automatic Speech Segmentation in French / Segmentação automática da fala em francês
Bibliographic record
Abstract
Abstract: Whether we read aloud or silently, we segment speech not in words, but in accent phrases, i.e. sequences containing only one stressed syllable (excluding emphatic stress). In lexically stressed languages such as Italian or English, the location of stress in a noun, an adverb, a verb or an adjective (content words) is defined in the lexicon, and accent phrases include one single content word together with its associated grammatical words. In French, a language deprived from lexical stress, accent phrases are defined by the time it takes to read or pronounce them. Therefore, actual phrasing, i.e. the segmentation into accent phrases, depends strongly on the speech rate chosen by the speaker or the reader, whether in oral or silent reading mode. With a slow speech rate, all content words form accent phrases whose final syllables are stressed, whereas a fast speech rate could merge up to 10 or 11 syllables together in a single accent phrase with more than one content word. Based on this observation, and on other properties of stressed syllables, a computer algorithm for automatic phrasing, operating in a top-down fashion, is presented and applied to two examples of read and spontaneous speech.Keywords: accent phrase; French; phrasing; stress location; boundary detection.Resumo: Quando lemos em voz alta ou silenciosamente, segmentamos a fala em palavras, mas em grupos acentuais, i.e., sequências contendo uma única sílaba acentuada (excluindo-se acento enfático). Em línguas lexicalmente acentuadas como o italiano ou o inglês, a localização do acento em um substantivo, um advérbio, um verbo ou em um adjetivo (palavras lexicais) é definida no léxico, e sintagmas acentuais incluem uma única palavra lexical, acompanhada das palavras gramaticais a ela associadas. Em francês, uma língua que não possui acento lexical, sintagmas acentuais são definidos pelo tempo que se leva para lê-los ou pronunciá-los. Assim, os constituintes concretos, i.e., a segmentação em grupos acentuais, depende fortemente da velocidade de fala escolhida pelo falante ou leitor, tanto na fala como na leitura silenciosa. Com uma velocidade de fala baixa, todas as palavras lexicais formam grupos acentuais cujas sílabas finais são acentuadas, enquanto o ritmo de fala rápido poderia juntar de 10 a 11 sílabas em um mesmo grupo acentual contendo mais de uma palavra lexical. Com base nessa observação e em outras propriedades das sílabas acentuadas, um algoritmo computacional para segmentação automática, atuando de maneira top-down é apresentado e aplicado a dois exemplos de leitura e fala espontânea.Palavras-chave: grupo acentual; francês; segmentação; posição do acento; detecção de fronteira.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".