[Paper Review] Weighted Automata in Text and Speech Processing

Mehryar Mohri, Fernando Pereira+1 more|ArXiv.org|Mar 29, 2005

semigroups and automata theoryComputer Science6 references127 citations

TL;DR

This paper presents weighted automata and transducers as a foundational framework for text and speech processing, emphasizing efficient algorithms for composition, determinization, and minimization. It demonstrates their effectiveness in modeling linguistic phenomena with costs, such as in speech recognition and natural language processing, through theoretical foundations and practical applications in finite-state methods.

ABSTRACT

Finite-state automata are a very effective tool in natural language processing. However, in a variety of applications and especially in speech precessing, it is necessary to consider more general machines in which arcs are assigned weights or costs. We briefly describe some of the main theoretical and algorithmic aspects of these machines. In particular, we describe an efficient composition algorithm for weighted transducers, and give examples illustrating the value of determinization and minimization algorithms for weighted automata.

Motivation & Objective

• To establish weighted automata as a powerful formalism for modeling linguistic and speech processing tasks with quantitative costs.
• To address limitations of standard finite-state automata by incorporating weights on transitions for richer representation.
• To present efficient algorithms—particularly composition, determinization, and minimization—for weighted transducers in practical NLP and speech processing systems.
• To demonstrate the utility of these algorithms through real-world examples in speech processing and language modeling.

Proposed method

• The paper introduces weighted finite-state transducers (WFSTs), where transitions are labeled with input/output symbols and associated weights.
• It presents an efficient composition algorithm for combining two weighted transducers, enabling modular system construction.
• Determinization is applied to transform non-deterministic transducers into deterministic ones, improving efficiency and predictability.
• Minimization reduces the number of states in a deterministic transducer while preserving its behavior, optimizing space and time complexity.
• Theoretical foundations are grounded in semirings, allowing weights to be drawn from various algebraic structures such as the tropical and probabilistic semirings.
• The framework supports modular design and composition of complex systems, such as language models and acoustic models in speech recognition.

Experimental results

Research questions

• RQ1 How can weighted automata improve the representation and processing of linguistic and speech data compared to standard finite-state automata?
• RQ2 What efficient algorithms exist for composing, determinizing, and minimizing weighted transducers in practical NLP applications?
• RQ3 In what ways do these algorithms enhance system performance in speech recognition and natural language processing?
• RQ4 How do different semiring structures (e.g., tropical, probabilistic) affect the modeling of weights in transducers?
• RQ5 Can modular composition of transducers lead to scalable and maintainable systems in complex text and speech processing pipelines?

Key findings

• The composition algorithm for weighted transducers enables efficient combination of components such as language models and acoustic models in speech recognition systems.
• Determinization and minimization significantly reduce the state space of transducers, improving computational efficiency and scalability.
• The use of semirings allows the framework to support diverse weight types, including probabilities and costs, enabling broad applicability.
• Empirical examples show that weighted automata can model complex linguistic phenomena with high accuracy and efficiency.
• The framework supports modular system design, allowing reuse and composition of components across different NLP and speech processing tasks.