NeurIPS Conference 2006 Conference Paper
- John Hershey
- Trausti Kristjansson
- Steven Rennie
- Peder Olsen
Human listeners have the extraordinary ability to hear and recognize speech even when more than one person is talking. Their machine counterparts have historically been unable to compete with this ability, until now. We present a modelbased system that performs on par with humans in the task of separating speech of two talkers from a single-channel recording. Remarkably, the system surpasses human recognition performance in many conditions. The models of speech use temporal dynamics to help infer the source speech signals, given mixed speech signals. The estimated source signals are then recognized using a conventional speech recognition system. We demonstrate that the system achieves its best performance when the model of temporal dynamics closely captures the grammatical constraints of the task. One of the hallmarks of human perception is our ability to solve the auditory cocktail party problem: we can direct our attention to a given speaker in the presence of interfering speech, and understand what was said remarkably well. Until now the same could not be said for automatic speech recognition systems. However, we have recently introduced a system which in many conditions performs this task better than humans [1][2]. The model addresses the Pascal Speech Separation Challenge task [3], and outperforms all other published results by more than 10% word error rate (WER). In this model, dynamics are modeled using a layered combination of one or two Markov chains: one for long-term dependencies and another for short-term dependencies. The combination of the two speakers was handled via an iterative Laplace approximation method known as Algonquin [4]. Here we describe experiments that show better performance on the same task with a simpler version of the model. The task we address is provided by the PASCAL Speech Separation Challenge [3], which provides standard training, development, and test data sets of single-channel speech mixtures following an arbitrary but simple grammar. In addition, the challenge organizers have conducted human-listening experiments to provide an interesting baseline for comparison of computational techniques. The overall system we developed is composed of the three components: a speaker identification and gain estimation component, a signal separation component, and a speech recognition system. In this paper we focus on the signal separation component, which is composed of the acoustic and grammatical models. The details of the other components are discussed in [2]. Single-channel speech separation has previously been attempted using Gaussian mixture models (GMMs) on individual frames of acoustic features. However such models tend to perform well only when speakers are of different gender or have rather different voices [4]. When speakers have similar voices, speaker-dependent mixture models cannot unambiguously identify the component speakers. In such cases it is helpful to model the temporal dynamics of the speech. Several models in the literature have attempted to do so either for recognition [5, 6] or enhancement [7, 8] of speech. Such models have typically been based on a discrete-state hidden Markov model (HMM) operating on a frame-based acoustic feature vector. Modeling the dynamics of the log spectrum of speech is challenging in that different speech components evolve at different time-scales. For example the excitation, which carries mainly pitch, versus the filter, which consists of the formant structure, are somewhat independent of each other. The formant structure closely follows the sequences of phonemes in each word, which are pronounced at a rate of several per second. In non-tonal languages such as English, the pitch fluctuates with prosody over the course of a sentence, and is not directly coupled with the words being spoken. Nevertheless, it seems to be important in separating speech, because the pitch harmonics carry predictable structure that stands out against the background. We address the various dynamic components of speech by testing different levels of dynamic constraints in our models. We explore four different levels of dynamics: no dynamics, low-level acoustic dynamics, high-level grammar dynamics, and a layered combination, dual dynamics, of the acoustic and grammar dynamics. The grammar dynamics and dual dynamics models perform the best in our experiments. The acoustic models are combined to model mixtures of speech using two methods: a nonlinear model known as Algonquin, which models the combination of log-spectrum models as a sum in the power spectrum, and a simpler max model that combines two log spectra using the max function. It turns out that whereas Algonquin works well, our formulation of the max model does better overall. With the combination of the max model and grammar-level dynamics, the model produces remarkable results: it is often able to extract two utterances from a mixture even when they are from the same speaker 1. Overall results are given in Table 1, which shows that our closest competitors are human listeners. Table 1: Overall word error rates across all conditions on the challenge task. Human: average human error rate, IBM: our best result, Next Best: the best of the eight other published results on this task, and Chance: the theoretical error rate for random guessing. System: Word Error Rate: Human 22. 3% IBM 22. 6% Next Best 34. 2% Chance 93. 0%