Normalization and Transformation Techniques for Robust Speaker Recognition
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Recognizing a person's identity by voice is one of intrinsic capabilities for human beings. Automatic speaker recognition (SR) is a computational task for computers to perform a similar task, i.e., to recognize human identity based on voice characteristics. By taking a voice signal as input, automatic speaker recognition systems extract distinctive information from the input, usually using signal processing techniques, and then recognize a speaker's identity based on the extracted information by comparing it with the knowledge previously learned at a training stage. The extracted distinctive information is encoded in a sequence of feature vectors, which is referred to as frame sequence. In terms of purposes of applications, SR tasks can be classified into two categories: speaker identification and speaker verification. Speaker identification (SI) is an application to recognize a speaker's identity from a given group of enrolled speakers. If a speaker is assumed to be always in the enrolled speaker group, it is referred to as the closed set speaker identification; Otherwise, it is referred to as the open set speaker identification. On the other hand, speaker verification (SV) is an application to verify a speaker identity by simply making a binary decision, i.e., answering an identity question by either yes or no. SV is one of biometric authentication techniques, along with others, such as fingerprint In the past decades, a variety of techniques for modeling and decision-making have been proposed to speaker recognition and proved to work effectively to some extent. In this chapter, we shall not delve too much into the survey for these techniques, but rather focus on normalization and transformation techniques for robust speaker recognition. For a tutorial of the conventional modeling and recognizing techniques, the reader can refer to (Campbell, 1999; Here, we just make it explicit that among many techniques the most successful ones are Gaussian mixture model (GMM) and hidden Markov model (HMM). With GMM/HMM, high performance can be achieved in sound working conditions, such as in a quiet environment, and for broadband speech. However, these techniques run into problems in realistic applications, since many realistic applications can not always satisfy the requirements of clean and quiet environments. Instead, the working environments are more adverse, noisy and sometimes in narrow-band width, for instance, telephony speech. Most SR systems degrade their performance substantially in adverse conditions. To deal with the difficulties, robust speaker recognition is such a topic for study.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle