BackgroundThe development of automatic tools based on acoustic analysis allows to overcome the limitations of perceptual assessment for patients with head and neck cancer. The aim of this study is to provide a systematic review of literature describing the effects of oral and oropharyngeal cancer on speech intelligibility using acoustic analysis.MethodsTwo databases (PubMed and Embase) were surveyed. The selection process, according to the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) statement, led to a final set of 22 articles.ResultsNasalance is studied mainly in oropharyngeal patients. The vowels are mostly studied using formant analysis and vowel space area, the consonants by means of spectral moments with specific parameters according to their phonetic characteristic. Machine learning methods allow classifying “intelligible” or “unintelligible” speech for T3 or T4 tumors.ConclusionsThe development of comprehensive models combining different acoustic measures would allow a better consideration of the functional impact of the speech disorder.