Issue 4 • May 2002
Cited by: Papers (2)
Discriminative training of hidden Markov models (HMMs) using minimum classification error training (MCE) has been shown to work well for certain speech recognition applications. MCE is, however, somewhat prone to overspecialization. This study investigates various techniques which improve performance and generalization of the MCE algorithm. Improvements of up to 10% in relative error rate on the t... View full abstract»
Cited by: Papers (9)
A joint source-channel coding system for transmitting speech on a bandlimited additive white Gaussian noise (AWGN) channel is presented. The proposed method uses a hybrid of digital and analog modulation techniques. The digital part of the system consists of a Federal Standard 1016 code-excited linear predictive (FS 1016 CELP) speech coder followed by a rate-3/5 parallel concatenated (turbo) error... View full abstract»
Cited by: Papers (17)
In many noise control applications, the noise is dominated by low frequencies and generated by several independent periodic sources. In such situations the tonal noise may be suppressed by using a narrowband multiple-reference feedforward controller. The performance characteristics of the control system, e.g., the convergence behavior and noise reduction are directly related to the controller adap... View full abstract»
Cited by: Papers (91)
Modern speaker recognition applications require high accuracy at low complexity. We propose the use of a polynomial-based classifier to achieve these objectives. This approach has several advantages. First, polynomial classifier scoring yields a system which is highly computationally scalable with the number of speakers. Second, a new training algorithm is proposed which is discriminative, handles... View full abstract»
Aims & Scope
Covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language.
This Transactions ceased publication in 2005. The current retitled publication is IEEE/ACM Transactions on Audio, Speech, and Language Processing.