Feedback on pronunciation is vital for spoken language teaching. Automatic pronunciation evaluation and feedback can help non-native speakers to identify their errors, learn sounds and vocabulary, and improve their pronunciation performance. Such speech recognition can be performed using Sphinx trained on a database of native exemplar pronunciation and non-native examples of frequent mistakes. Adaptation techniques based on such databases can obtain better recognition of non-native speech. Pronunciation scores can be calculated for each phoneme, word, and phrase by means of Hidden Markov Model alignment with the phonemes of the expected text. In addition to such acoustic alignment scores, we can also use edit distance scoring to compare the scores of the spoken phrase with those of models for various mispronunciations and alternate correct pronunciations. These scores may be augmented with factors such as expected duration and relative pitch to achieve more accurate agreement with expert phoneticians’ average manual subjective pronunciation scores. We are building such a system using the CMU Sphinx3 speech recognition system.