ACM Transactions on Speech and Language Processing (TSLP)

ACM Transactions on Speech and Language Processing (TSLP) is an ACM journal dedicated to advancing research in computational speech and language technologies. First launched in 2004, the journal offered a quarterly forum for peer‑reviewed research that addresses key challenges in speech recognition, language understanding and generation, dialogue systems, machine translation, spoken and text retrieval, and related areas. It was indexed in the ACM Digital Library and assigned print ISSN 1550‑4875 and online ISSN 1550‑4883. Articles in TSLP encompass both foundational theory and applied system development, emphasizing novel methods, rigorous evaluation, and practical relevance in building intelligent language systems capable of robust performance across diverse tasks and domains. Contributions typically include algorithmic innovations, comparative evaluations against benchmarks, integration of machine learning frameworks with traditional speech and language models, and applications that span conversational agents, summarization systems, semantic parsing, and multimodal language processing. TSLP’s editorial board comprised experts in computational linguistics, speech science, and machine learning, ensuring high standards of review and scholarly impact. Around 2014, TSLP was merged with the IEEE Transactions on Audio, Speech and Language Processing into a joint IEEE/ACM Transactions on Audio, Speech, and Language Processing, reflecting the convergence of research communities and broader scope of the merged journal.

ACM Transactions on Speech and Language Processing (TSLP) is :-

• International, Peer-Reviewed, Open Access, Refereed, Computational Mathematics, Computer Science (miscellaneous), design, development, evaluation, and integration of computational techniques that support automatic speech recognition, spoken dialogue, natural language understanding and generation, language modeling, machine translation, discourse analysis, spoken and written document retrieval, related machine learning methods, The journal’s articles include theoretical advances, algorithmic innovations, experimental evaluations , Online or Print , Quarterly Journal

• UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1550-4875, E-ISSN: 1550-4883, Established: 2004,
• Does Not Provide Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE