diff --git a/nemo/collections/multimodal/speech_llm/modules/common/audio_text_generation_strategy.py b/nemo/collections/multimodal/speech_llm/modules/common/audio_text_generation_strategy.py
index b113a5717f29..f6b190952d2c 100644
--- a/nemo/collections/multimodal/speech_llm/modules/common/audio_text_generation_strategy.py
+++ b/nemo/collections/multimodal/speech_llm/modules/common/audio_text_generation_strategy.py
@@ -257,6 +257,8 @@ def init_batch_per_step(
                 max_steps_with_same_token = 10
                 step_count = 0
                 decoder_mems_list = self.extra_outputs.get('decoder_mems_list', None)
+                exclude_sink_frames = strategy_args.get('exclude_sink_frames', 2)
+                exclude_sink_frames = exclude_sink_frames // 2
                 while apply_xatt and step_count < max_steps_with_same_token:
                     encoder_input, self.extra_outputs = self.model.perception_cross_attn(
                         speech_encoded,
@@ -272,9 +274,11 @@ def init_batch_per_step(
                     xatt_scores = self.extra_outputs['xatt_scores_list'][strategy_args['xatt_layer']]
                     xatt_scores = torch.mean(xatt_scores, 1)
                     # exclude first audio tokens (sink tokens)
-                    most_attended_idx = torch.argmax(xatt_scores[:,:,2:], dim=-1)+2
-                    average_pooling_xatt_scores = self.pool2d(xatt_scores[:,:,2:])
-                    most_attended_idx_pool = torch.argmax(average_pooling_xatt_scores, dim=-1)+2
+                    # most_attended_idx = torch.argmax(xatt_scores[:,:,2:], dim=-1)+2
+                    # alternative serach for the most attended frame by smoothing the attention scores
+                    most_attended_idx = torch.argmax(xatt_scores[:,:,exclude_sink_frames:], dim=-1)+exclude_sink_frames
+                    average_pooling_xatt_scores = self.pool2d(xatt_scores[:,:,exclude_sink_frames:])
+                    most_attended_idx_pool = torch.argmax(average_pooling_xatt_scores, dim=-1)+exclude_sink_frames
                     if strategy_args["debug_mode"]:
                         logging.warning(f"=== most_attended_idx: {most_attended_idx[0].item()}")
                         logging.warning(f"=== most_attended_idx_pool: {most_attended_idx_pool[0].item()}")  
@@ -372,7 +376,7 @@ def init_batch_per_step(
                 self.attention_mask = self.model._create_attention_mask(encoder_input)
             context_tokens = self.context_tokens
         else:
-            raise NotImplementedError("This function is not implemented yet")
+            # raise NotImplementedError("This function is not implemented yet")
             batch = {
                 'audio_signal': audio_signal,
                 'audio_signal_length': audio_length,
@@ -480,6 +484,7 @@ def prepare_batch_at_step(
                 max_steps_with_same_token = 10 # max number of speech increasing steps with the same token
                 step_count = 0
                 decoder_mems_list = self.extra_outputs.get('decoder_mems_list', None)
+                exclude_sink_frames = strategy_args.get('exclude_sink_frames', 2)
 
                 while apply_xatt and step_count < max_steps_with_same_token:
                     embeddings2use_tmp, self.extra_outputs_tmp = self.model.perception_cross_attn(
@@ -497,11 +502,12 @@ def prepare_batch_at_step(
                     xatt_scores = self.extra_outputs_tmp['xatt_scores_list'][strategy_args['xatt_layer']]
                     xatt_scores = torch.mean(xatt_scores, 1) # mean by attention heads
                     # most_attended_idx = torch.argmax(xatt_scores, dim=-1)
-                    most_attended_idx = torch.argmax(xatt_scores[:,:,8:], dim=-1)+8
+                    most_attended_idx = torch.argmax(xatt_scores[:,:,exclude_sink_frames:], dim=-1)+exclude_sink_frames
                     sum_of_last_xatt = torch.sum(xatt_scores[:,:, -strategy_args['alignatt_thr']:])
 
-                    average_pooling_xatt_scores = self.pool2d(xatt_scores[:,:,8:])
-                    most_attended_idx_pool = torch.argmax(average_pooling_xatt_scores, dim=-1)+8
+                    # alternative serach for the most attended frame by smoothing the attention scores
+                    average_pooling_xatt_scores = self.pool2d(xatt_scores[:,:,exclude_sink_frames:])
+                    most_attended_idx_pool = torch.argmax(average_pooling_xatt_scores, dim=-1)+exclude_sink_frames
 
                     if strategy_args["debug_mode"]:
                         logging.warning(f"self.cur_speech_encoded_len: {self.cur_speech_encoded_len.item()}")