{"id":35276,"date":"2025-01-16T07:16:49","date_gmt":"2025-01-16T06:16:49","guid":{"rendered":"https:\/\/www.markt-kom.com\/?p=229010"},"modified":"2025-01-16T07:42:36","modified_gmt":"2025-01-16T06:42:36","slug":"automatische-spracherkennung-macht-grosse-fortschritte","status":"publish","type":"post","link":"https:\/\/www.organisator.ch\/it\/marketing\/2025-01-16\/automatische-spracherkennung-macht-grosse-fortschritte\/","title":{"rendered":"Il riconoscimento vocale automatico fa grandi progressi"},"content":{"rendered":"
\"\"<\/a>\r\n
I moderni sistemi di riconoscimento vocale raggiungono una precisione impressionante in ambienti rumorosi. (Immagine simbolica: Unsplash.com)<\/figcaption>\r\n<\/figure>\r\n

In un recente studio, la linguista computazionale Eleanor Chodroff dell'UZH e Chloe Patman dell'Universit\u00e0 di Cambridge hanno analizzato la capacit\u00e0 dei moderni sistemi ASR di affrontare condizioni di ascolto difficili. I sistemi testati erano \"wav2vec 2.0\" di Meta e \"Whisper large-v3\" di OpenAI. Il punto di riferimento: le prestazioni dei madrelingua britannici.<\/p>\r\n

I test si sono svolti in condizioni estreme: dal rumore simile a quello del parlato al rumore realistico del pub, sia con che senza maschera di cotone. Il risultato: gli esseri umani hanno ottenuto i risultati migliori in generale, ma il sistema OpenAI \"Whisper large-v3\" li ha superati in quasi tutti gli scenari. Solo nel rumore dei pub \u00e8 stato alla pari con l'udito umano.<\/p>\r\n

Particolarmente sorprendente \u00e8 stata la capacit\u00e0 di \"Whisper large-v3\" di elaborare correttamente il parlato anche senza un supporto contestuale.<\/p>\r\n

La differenza decisiva<\/strong><\/h3>\r\n

Le enormi prestazioni di \"Whisper\" si basano su quantit\u00e0 gigantesche di dati di addestramento. Mentre \"wav2vec 2.0\" di Meta \u00e8 stato addestrato con 960 ore di dati vocali, OpenAI ha utilizzato oltre 75 anni di dati vocali per il suo sistema standard. Il modello pi\u00f9 potente ha utilizzato addirittura pi\u00f9 di 500 anni di dati vocali. In confronto, gli esseri umani sviluppano competenze simili in pochi anni: un aspetto notevole, come sottolinea la responsabile dello studio Eleanor Chodroff. \"Inoltre, il riconoscimento automatico del parlato in quasi tutte le altre lingue rimane una sfida importante\".<\/p>\r\n

Diverse fonti di errore<\/strong><\/h3>\r\n

Lo studio ha anche dimostrato che gli esseri umani e le macchine falliscono in modi diversi. Gli esseri umani creano quasi sempre frasi grammaticalmente corrette, ma spesso scrivono frammenti di frasi. Al contrario, \"wav2vec 2.0\" ha spesso generato frasi incomprensibili in condizioni difficili. \"Whisper\" \u00e8 stato in grado di produrre frasi grammaticalmente corrette, ma ha riempito i vuoti nel contenuto con informazioni completamente errate.\u00a0(pd\/swi)<\/em><\/p>\r\n

\r\n

Il Studio \"Speech recognition in adverse conditions by humans and machines\" di Chloe Patman e Eleanor Chodroff<\/a> pu\u00f2 essere letto in dettaglio qui. <\/em><\/p>","protected":false},"excerpt":{"rendered":"

Possono gli esseri umani o le macchine riconoscere meglio il parlato? In ambienti rumorosi come i pub, i moderni sistemi di riconoscimento vocale (ASR) raggiungono un'accuratezza impressionante e, in alcuni casi, superano addirittura gli esseri umani. Ma questo dimostra quanto siano effettivamente notevoli le prestazioni umane.<\/p>","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_focuskw":"Spracherkennung","_yoast_wpseo_metadesc":"Mensch oder Maschine: Wer erkennt Sprache besser? 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