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BEHAVOX

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Behavox-forskning i kontinuerlig læring med på International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Behavox , som udbyder en række sikkerhedsprodukter, der hjælper compliance-, HR- og sikkerhedsafdelingerne med at beskytte deres virksomhed og kolleger mod ondsindede aktører, fortæller i dag, at dens akademiske forskningsdokument om kunstig intelligens "kontinuerlig læring med lattice-fri MMI til stemmegenkendelse" er blevet accepteret af International Conference on Acoustics, Speech, and Signal Processing (ICASSP). ICASSP er verdens største og mest omfattende tekniske konference med fokus på signalbehandling og tilhørende anvendelse. Behavox præsenterede den 9. maj 2022 dette dokument i Singapore for IEEE ICASSP 2022 .

"Nogle gange forsker videnskabsfolk for videnskabens skyld, og samfundet oplever ikke nogen umiddelbare fordele af forskningen. Vi har derimod skubbet videnskaben til en ny grænse for at finde en løsning på et aktuelt problem," siger Behavox's CTO Joseph Benjamin. "Vi er beærede over at have modtaget anerkendelse fra en så prestigefyldt konference og er meget stolte af vores team og det arbejde, folkene har udført."

Dokumentet, der er forfattet af Behavox's maskinlæringsteam, ser på udfordringerne i forbindelse med løbende forbedring af stemmegenkendelsessystemer i miljøer, der kræver beskyttelse af personoplysninger. Vores forskning har direkte forbindelse til Behavox's FoU-tilgang, der giver kunderne den bedste transskriptionskvalitet på det finansielle marked. I FinTech sker der hele tiden ændringer i det sprog og de data, som Behavox-systemerne hurtigt og pålideligt skal behandle, og stigningen i kryptovalutaer og videokommunikation er blot to eksempler.

Derfor skal maskinlæringsmodeller jævnligt opdateres med relevante data. For at gøre sådanne opdateringer mulige, uden at sætte databeskyttelsen over styr, skal modellen gradvist kunne forbedres i sikre klientmiljøer uden adgang til historiske data og data fra andre kunder. Dette er en alvorlig udfordring inden for maskinlæring på grund af den katastrofale glemsomhed, hvor modellen, efter at have forbedret en bestemt type data, begynder at præstere dårligt på tidligere lærte data.

ICASSP-dokumentet fra Behavox bidrager til den fortsatte læring til brug ved stemmegenkendelse. Forfatterne foreslår en ny algoritme, der udvider og forfiner den almindeligt anvendte LWF-teknik (Learning Without Forgetting), der er afhængig af en regulariseringsterm og tvinger neurale netværk til at mindske glemsomhed. Mere specifikt designede forfatterne en nyhed i form af neurale netværk med indlæring af tab på sekvensniveau, der kan bruges i stedet for den konventionelle, punktvise LWF. De viste en betydelig reduktion i glemsomhed, når man finjusterede det neurale netværk sekventielt efter forskellige accenter- og taleformer.

Behavox har konstateret, at brugen af stemmeplatforme med årene er steget 94 procent, og at næsten en tredjedel af al kommunikation på arbejdspladsen i gennemsnit foregår som telefonmøder eller videokonferencer.

Denne teknik er allerede blevet anvendt med succes på dets danske system, hvor samarbejdet mellem Behavox's lingvister, maskinlæringsteamet og en kunde gav Behavox mulighed for at opbygge sit første danske transskriptionssystem, der arbejder pålideligt med bankdata.

Om Behavox

Behovox udbyder en række sikkerhedsprodukter, der hjælper compliance-, HR- og sikkerhedsafdelingerne med at beskytte deres virksomhed og kolleger mod ondsindede aktører.

Med AI-baseret analyse af alle virksomhedens kommunikationsdata, herunder e-mail, chat, telefonmøde- og videokonferenceplatforme, hjælper Behavox virksomhederne med at identificere ulovlig, umoralsk og skadelig adfærd på arbejdspladsen.

Behavox, der blev grundlagt i 2014, har hovedkvarter i Montreal og kontorer i New York, London, Seattle, Singapore og Tokyo.

Originalsprogsudgaven af denne bekendtgørelse er den officielle, autoriserede version. Oversættelserne er kun tænkt som en hjælp og bør sammenholdes med kildesprogsteksten, der som den eneste er juridisk bindende.

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