In this paper, we describe advances made in transcribing speech from witnesses and judges in Bastarache and Charbonneau commissions in Quebec. This is a rich audio with many speakers speaking fluently in conversational style. We extract features from SSL models to fine tune hybrid HMM/DNN models and also end-to-end models to compare word error rate (WER) on Bastarache and Charbonneau commission test sets. We also try SSL features extracted at both 10 ms and 20 ms temporal resolution for comparison. In a previous paper, we showed that we reduce WER for all the 15 low resource languages in OpenASR21 evaluation (with only 10 h of training audio), when we increase the temporal resolution of feature parameters computed from the speech SSL models from 20 ms to 10 ms. In this paper, we experiment with Quebec French data with 10 ms and 20 ms temporal resolution. For a training set of 472 h, we show that we still benefit from increasing the temporal resolution from 20 ms to 10 ms. Also, hybrid DNN/HMM models give lower word error rate (WER) than end-to-end speech recognition with 472 h of training audio. With a training set over 1000 h of audio, the end-to-end ASR system gives similar WER as the hybrid DNN/HMM system, and there is no significant improvement with increasing temporal resolution. We also compare our results with Whisper, an automatic speech recognition (ASR) system trained on 680,000 h of multilingual and multitask supervised data collected from the web.